Lubrication management rarely makes headlines. It is not the shiny part of maintenance that attracts new talent, budgets or conference stages. But on the shop floor, it is often the difference between stable production and recurring, expensive surprises.

That perspective is shaped by years spent close to work. Technical Advisor Aleksi Nykänen has built his career around lubrication management and condition monitoring, first inside large industrial operations and later in customer-facing development work. Technical Manager Mika Römpötti brings decades of hands-on experience across lubrication roles and long-term involvement in professional lubrication networks and industry guidance. They have seen what “good” looks like in mature organisations, and how quickly standards can erode when competence, ownership and routines are not protected.

Two Interflon specialists, Technical Advisor Aleksi Nykänen and Technical Manager Mika Römpötti, describe a pattern they keep seeing across industries: lubrication is treated as a routine task, not as a controlled process. The result is a long list of small, avoidable mistakes that accumulate into major reliability problems.

“People don’t necessarily lack effort. They lack a system,” Nykänen says. “Lubrication management is a wide, multi-part discipline. If the basics aren’t defined and controlled, everything becomes reactive.”

In practice, the issues start with fundamentals: storage, handling and cleanliness. Lubricants are exposed to dirt and moisture, containers are not clearly separated, and the same equipment may end up with multiple incompatible greases. Even when someone is “doing the rounds,” the lubricant may not reach the contact surfaces as intended.

A common visual tells the story: grease around the nipple and on surrounding surfaces, but not necessarily where it is needed. The environment gets messier, contamination increases, and the next lubrication cycle adds more dirt to the same area. It can feel like work is being done, yet the asset is being set up for failure.

Over-lubrication is another recurring theme. Without calculation and clear standards, manual lubrication, automatic or semi-automatic lubricators can deliver far more than required. Excess grease increases heat and drags, pushes past seals, attracts contaminants and creates a housekeeping problem that hides early warning signs.

“If you don’t calculate the right amount, you’re guessing,” Römpötti says. “And guessing often means too much.”

The consequences are not theoretical. Industry sources often cite that a significant share of premature bearing failures is linked to poor lubrication practices and contamination. When plants struggle with cost pressure and productivity targets, it is hard to justify waste that is both preventable and recurring. Yet the root causes are rarely about one bad decision. They are structural.

One factor is the loss of tacit knowledge. Many experienced technicians have retired, and their practical know-how did not transfer into documented standards, training or role definitions. In some organisations, it is not even clearly described what competence is expected from a person responsible for lubrication tasks.

“If you haven’t defined what ‘good’ looks like, how can you ensure the next person can deliver it?” Nykänen asks.

Another factor is education. Lubrication engineering is not widely covered in many technical programmes, and it is not perceived as an attractive speciality for young professionals. The result is a gap between the complexity of modern assets, and the training people receive before entering the field.

Then there is prioritisation. Many organisations talk about predictive maintenance, digitalisation and advanced analytics, but still operate in “firefighting mode.” Preventive routines are squeezed, and lubrication becomes a checkbox rather than a reliability lever. Without the real ownership of lubrication program, it is hard to get lubrication and the culture on the level where it should be.

The specialists are not anti-technology. They use condition monitoring, oil analysis energy consume measurements and existing customer data as part of improvement work. But they are sceptical of the idea that AI can “solve lubrication” if the underlying practices are inconsistent.

“AI can help you predict failures,” Römpötti says. “But it won’t go out there and fix the basics. If the fundamentals are poor, prediction just tells you what you already set in motion.”

There is also a less discussed contributor: equipment design and delivery. Even new machines often arrive without features that make lubrication control practical, or possible such as proper sight glasses for checking oil levels.

“When the only way to verify a level is to remove a plug, every inspection becomes a contamination risk”, Nykänen states.

Maintenance teams are then left with impossible instructions: check the oil level weekly, but do it without a safe, visual method. In the real world, the task is skipped, rushed or done in a way that introduces more dirt.

So, what does improvement look like when the goal is not to sell a product, but to change outcomes?

The approach described by Nykänen and Römpötti starts with a simple principle: do not begin in the deep end. First, make the basics visible and measurable: cleanliness, correct lubricant selection, correct quantities, correct intervals, correct tools and clear responsibilities. After the field is corrected, next step is to correct the content of tasks in the maintenance system.

Their work often begins on site, walking through assets with the people who perform lubrication tasks. The goal is to identify the most critical gaps and fix what is preventing good practice. Sometimes that means reorganising storage and labelling. Sometimes it means adding small hardware upgrades that reduce contamination risk and make inspection realistic. Sometimes it means harmonising lubricant portfolios that have grown uncontrolled over time.

“We want the frontline to succeed,” Nykänen says. “Not to create dependency on an external expert, but to build capability inside the plant.”

This is where training becomes less about classroom theory and more about empowering technicians to make decisions. The wins can be surprisingly small: a leaking gearbox that stops leaking without a major rebuilding, or a lubrication team that finally feels their work is recognised as critical.

“One of the best moments is when people thank you for listening,” Römpötti says. “They feel their work matters again, and then they start improving it themselves.”

The bigger message is uncomfortable but necessary: lubrication management is not a side task. It is an operational discipline that needs standards, competence, tools and leadership attention.

Plants that want higher reliability often invest in sensors, dashboards and analytics. Those investments can pay off, but only if the physical reality is under control. Cleanliness, correct lubrication and clear practices are not old-fashioned. They are prerequisites.

And perhaps that is the most thought-provoking part: the future of maintenance may be digital, but the future of reliability still depends on people doing the basics well.

The hidden cost of “good enough” lubrication

Poor lubrication is rarely a single mistake. It is usually a chain: contamination, wrong product, wrong quantity, and missing routines. The result shows up as repeat failures, wasted energy, and maintenance work that never seems to end.

• Lubrication errors and contamination can account for up to 30% of technical disturbances and around 40% of maintenance costs.

• Excess friction and wear can drive unnecessary energy use. With better lubrication practices, energy consumption linked to friction and wear could be reduced by about 10%.

Source: Interflon

If those numbers feel high, ask a simpler question: how many of your recurring issues are truly “mysterious” once you look at lubricant cleanliness, correct quantities, and basic inspection access?

Lubrication management: a practical reset checklist

1. Define what “good” looks like: roles, competence expectations, standards
2. Control cleanliness: storage, handling, transfer, sealing
3. Harmonise lubricants: avoid uncontrolled product sprawl
4. Calculate quantities: reduce over-lubrication and waste
5. Make inspection safe: sight glasses, sampling points, practical access
6. Use data wisely: oil analysis and condition monitoring to support, not replace, basics
7. Build capability: train and coach the people who do the work

Text: Mia Heiskanen Photos: Interflon

During the webinar, experts from INX International Ink Co., AssetWatch, and Noria Corporation emphasised that tools and technologies, while important, aren’t what ultimately drive success. The real differentiator is an organisation’s ability to align its people, processes, and daily discipline around reliability.

To better understand the impact of the transformation, Maintworld spoke with INX International about changes to the company’s daily operations.

INX International is a global manufacturer of inks and coatings for packaging, commercial, and digital printing. In high-volume production, such as metal decorating, consistent transfer, reliable curing, and stable operation are crucial to uptime. Any performance fluctuation affects the line and supply chain immediately.

INX launched its predictive maintenance strategy in 2023 with a focused pilot at its Charlotte site. Rather than implement tools everywhere, the team first established standard templates, clear asset structures, and consistent processes.

Building on support from digital platforms and condition-monitoring partners, the model was subsequently replicated across other U.S. sites in months.
A key step in this expansion was connecting real-time production data with condition monitoring systems to support maintenance decisions based on actual equipment behaviour. By integrating platforms such as AssetWatch for equipment condition monitoring and Oden for production data visibility, INX aligned maintenance activity with how equipment is running in real time.

“This approach moves maintenance from fixed schedules to interventions based on usage, load, and early wear indicators. Instead of servicing equipment by calendar, teams respond to measurable performance changes,” company officials said.

“In practice, this reduces unnecessary maintenance, improves response time to developing issues, and supports more stable operation across the production environment. A major advantage is the ability to identify early indicators of failure, allowing intervention before unplanned downtime impacts production.”

A central message of the webinar: reliability efforts fail if organisations focus on complex tools before establishing basic, consistent processes.

Many organisations introduce predictive maintenance tools, sensors, or analytics across different sites in parallel. While this can deliver short-term improvements, it often creates fragmentation. Different plants start working in different ways, using different definitions of failure, and measuring success differently. The speakers emphasised that standardisation is essential for successful transformation. This means defining key assets, identifying clear failure modes, and maintaining consistent practices across all sites.

This isn’t about restricting teams, but about creating a shared language. When everyone evaluates equipment consistently, improvements scale rather than being recreated at each site.

Establishing a foundation is essential; only then does it make sense to add advanced tools, such as vibration monitoring or predictive analytics. Otherwise, even the best systems struggle to deliver consistent results.

Turning data into action, not just dashboards, matters. Building on this, another key webinar theme was closing the gap between data collection and action.

Most organisations today are not short of information. Sensors, dashboards, and alerts are everywhere. Yet many still struggle with the same issue: data that is seen but not acted upon.

The webinar made a key point: data alone doesn’t improve reliability, action does. Value comes when teams respond swiftly and reliably to data, such as inspecting equipment early, adjusting lubrication, or scheduling preventive maintenance.

A strong indicator of maturity is response time. As organisations improve, they do not just collect more data, they react to it faster and with greater confidence.

Successful organisations treat data as a trigger for behaviour, not just information for review. When a system flags early signs of equipment degradation, the value lies not in the alert itself but in whether maintenance teams investigate, validate, and act on it in a timely manner. Over time, this creates ‘repeatable wins’: not isolated successes but steadily fewer unexpected breakdowns.

“The feedback loop trains the AI over time. As it learns, alerts become fewer and more refined, providing real signals amid the noise. It requires patience—many give up too soon—but persistence pays off,” says Borpit Intawiwat, Vice President, Engineering, INX International.

While processes and tools dominated much of the technical discussion, culture repeatedly emerged as the real deciding factor.
Reliability programs often fail not because technology is lacking, but because the organisation is not aligned. Maintenance, operations, and management may share goals but have different priorities and incentives.

“Even the best systems can’t overcome misalignment or lack of trust between teams,” one speaker said. As INX International’s VP of Operational Excellence, Chris Rodgers, told Maintworld: “You can install the best monitoring systems, but without alignment and trust between maintenance and operations, the impact remains limited.”

Reliability is ultimately about organisational culture, not just technology. INX’s focus was to use data for faster, better decisions—not simply to increase information. Reliability only improves when data leads to action. When maintenance and production teams share the same data and jointly interpret it, decisions become less reactive and more collaborative. Instead of debating opinions, teams can focus on what the equipment is indicating.

A further shift occurs when frontline teams use reliability tools proactively rather than waiting for instructions. This behavioural change—though small—often signals that reliability is now routine, no longer a separate initiative.

The webinar also touched on a common blind spot: reliability’s human side.

With more predictable maintenance, work environments change: emergency callouts drop, planned work rises, and stress falls. Technicians spend less time reacting and more time improving.

This shift directly affects work-life balance and job satisfaction. In some examples discussed, improved reliability allowed technicians to spend less time reacting to breakdowns and more time focusing on planned, value-added work. The result is not only operational improvement but also a healthier and more stable work environment.

Michael Patulski, Vice President, Paste Operations told Maintworld that recent system integrations and AI have boosted employee retention and streamlined recruiting at INX Charlotte. According to Patulski, AI drives employee development with targeted, real-time learning. These tools speed up new-hire competency, strengthen ongoing training, and accelerate career growth.

“AI has created advanced career paths and improved on-floor performance. INX Charlotte has seen logistics staff advance to frontline production supervision and machine operators move to production analytics. It’s exciting to witness accelerated career growth and high retention at INX Charlotte, Patulski concludes.

INX International

INX International is a wholly owned subsidiary of SAKATA INX worldwide operations, a $1.8 billion company established in 1896. The company is a global manufacturer of high-performance printing inks and coating for commercial, packaging, and digital print applications with full-service locations in North America, South America and Europe.

Lubrication: still the quiet failure point

Despite advances in monitoring technology, lubrication remains one of the most common—and preventable—causes of equipment failure, experts note.
The industry webinar Scaling Reliability with INX: Aligning People, Processes, and PdM, held on April 23, 2026, underscored a key insight: many lubrication failures stem not from poor-quality lubricants, but from contamination and inconsistent practices.
Dirt ingress, moisture, incorrect application, and poor storage practices continue to undermine equipment reliability—even in highly automated environments.

A structured lubrication program typically follows three steps:
1. Set clear targets – for example, cleanliness levels or contamination limits.
2. Define the right actions – such as improved storage, filtration, or application methods.
3. Measure consistently – through oil analysis and regular inspection.

What matters most is not the individual step, but the discipline of repeating the cycle. The speakers in the webinar also stressed that lubrication should not exist in isolation. When combined with vibration analysis, temperature monitoring, and other condition-based tools, it becomes part of a much more complete view of equipment health.

INX’s experience offers clear lessons for real-world reliability scaling:

• Focus on critical assets first –start where failure has the highest impact.
• Standardise early – build templates and shared structures before scaling.
• Invest in training continuously – not once, but repeatedly.
• Work with partners where it adds value – especially for specialised expertise.
• Make reliability shared ownership among operators, engineers, and maintenance teams.

Looking ahead, reliability systems are moving toward greater integration, bringing production data, condition monitoring, and maintenance management together rather than keeping them in separate tools.

The advantage is not increased complexity but less. Connected data lets teams spend less time searching and more time acting.

Text: Nina Garlo-Melkas Photos: INX International

Indicators behave within expected limits, processes unfold without friction, and the system feels readable, as if its logic is fully captured and brought under control. Nothing appears to be wrong—and that is precisely the problem. It is a comforting illusion, one engineering has cultivated with remarkable success.

Yet systems rarely betray themselves in obvious ways. Failures begin not with rupture, but with deviation—small, almost imperceptible shifts. A parameter drifts, a delay emerges, a dependency changes. Individually, these signals appear harmless. Together, they form patterns that are difficult to perceive, especially when observation tools continue to confirm what we expect to see. As long as these remain consistent, the system appears stable. But consistency is not truth: a system may remain coherent while gradually detaching from the reality it is meant to describe.

Maintenance has long been tasked with preserving that coherence, and for a time it succeeded. But today’s systems extend beyond what can be observed, entangled with volatile supply chains, energy systems, and evolving constraints. Control becomes less a property and more a temporary alignment between expectation and reality—one where risk does not emerge suddenly, but grows silently until it can no longer be ignored.

Legacy of Criticality: Maintenance has reinforced the illusion of control. It evolved to bring structure to uncertainty, transforming what once seemed unpredictable into something that could be classified and managed. The concept of criticality became one of its most important foundations. At its core, criticality offered a simple idea: not all assets matter equally. Some failures are negligible, others disruptive or costly. By ranking these differences, maintenance moved from reacting to failures to anticipating them, focusing effort where it mattered most. Criticality became the bridge between technical analysis and practical action, allowing organizations to allocate resources in a way that felt both efficient and justified.

For a long time, this approach worked remarkably well. In stable environments, where dependencies were understood, failure probability and consequence could be assessed with confidence. The system behaved within known limits, and maintenance strategies reflected that stability. But this stability was an assumption.

As the environment shifts, the assumption weakens. Assets once considered secondary can become critical—not because they change, but because the system around them does. A component gains importance when supply chains tighten, or processes lose their tolerance to disruption. Criticality does not disappear, but it becomes less stable. It no longer reflects an intrinsic property of the asset, but a relationship between the asset and an evolving context. When that context is not considered, decisions rely on assumptions that may no longer hold.

The concept itself is not flawed. It remains essential. But its traditional use implies a system that changes slowly, where priorities can be defined and periodically reviewed. What we now face is something different—a system in motion, where relevance is shaped not only by the asset, but also by its connections and dependencies.

Risk Is Not What We Thought: For a long time, risk appeared to be a concept we had domesticated. It could be expressed, calculated, and compared—probability on one side, consequence on the other—yielding a measure that could be ranked and acted upon. There was a sense that uncertainty, once quantified, could be contained within rational decision-making.

To a certain extent, this was true—at least, within the world for which that logic was designed. But as systems become more complex and less stable, this formulation captures only part of what risk actually is. It describes the likelihood of an event and the scale of its impact, yet says little about the conditions that make that impact manageable or catastrophic.

For decades, the role of maintenance was clear: prevent failure, reduce downtime, and optimize cost within stable operating conditions.

In practice, the same failure can lead to very different outcomes. A component may fail under identical conditions, yet its impact can range from negligible to severe—not because the failure has changed, but because the system that receives it has. What was once absorbed without difficulty may now propagate across tightly coupled processes. The event is the same; the system is not. Risk, therefore, is not simply a property of the asset or the failure mode. It emerges from the relationship between them and the environment in which they exist. It is shaped by interaction, dependency, and timing. It is fundamentally contextual. Like in Solaris, the system cannot be understood in isolation from the conditions that shape it.

This does not invalidate the traditional definition, but it exposes its limits. Probability and consequence remain essential, but they are no longer sufficient. Another dimension drives risk: how exposed the system is, how capable it is of absorbing disruption, and how dependent it has become on elements beyond its control. In practice, this dimension is often sensed rather than formalized.

When a maintenance engineer prioritizes an asset not because it fails often, but because “if it fails now, we are in trouble,” what is being assessed is not just likelihood or impact, but vulnerability—the system’s ability to cope at that moment. The challenge is not to calculate risk more precisely, but to understand it more completely.

The System Fights Back: At this point, the system stops behaving as we expect it to. The complexity of industrial systems is both structural and relational. Technical components, human decisions, organizational processes, and external constraints form a network where causality is distributed rather than localized. Failures rarely stem from a single cause; they emerge from multiple conditions aligning in ways difficult to anticipate. This challenges the fundamental assumption in maintenance that understanding individual failure modes is enough to understand system behavior.

It is still necessary, but is no longer sufficient. The system cannot be fully explained by its parts, because interactions between those parts generate behaviors that do not exist at the component level. The human is part of the network. The interpretations and actions of operators and decision-makers shape how failures unfold—sometimes stabilizing the system, sometimes contributing to its degradation. The boundary between human and technical elements is not fixed, but continuously negotiated.

Maintenance models still focus on what can be measured—failure rates, repair times, condition indicators—but critical dynamics emerge in layers that are harder to capture: timing, coordination, perception. This does not make control impossible, but it redefines it. Control cannot be achieved through reduction and classification. It requires understanding how the system behaves as a whole, how interactions evolve, and how risk is distributed across the network.

No longer confined to components or failure modes, risk is embedded in the system’s structure and dynamics. Managing it requires moving beyond isolated events and recognizing the system as an active, evolving entity—one that does not simply respond to interventions, but reshapes them.

Data Are Not the Answer: When complexity increases, the instinctive response is to gather more information. Systems that are harder to understand are observed more closely, measured more extensively, and monitored through an ever-growing network of sensors. Over time, this has led to an expansion in visibility: patterns can be detected earlier, degradation tracked in real time, and interventions scheduled with increasing precision. In this sense, the system appears more transparent than ever before.

The real failure is not when an asset stops working, but when the system cannot respond in time—when the gap between expectation and reality becomes too wide to manage.

But this transparency can be misleading. Data reveal what is happening within the asset, but not necessarily what it means for the system as a whole. A signal may indicate a developing fault, but its significance depends on factors beyond the data itself—operational constraints, dependencies, and the system’s ability to respond. Data require interpretation to become useful. Without context and judgment, more information does not bring understanding closer. Paradoxically, while failures are detected earlier, responses remain constrained by frameworks designed for a different context.

This is not a failure of technology, but of interpretation.

When the signals of Condition-Based Maintenance, for example, are not integrated into a broader understanding of risk—one that includes context and exposure—their value is limited. They inform, but they do not transform. What emerges is a distinction between knowing more and understanding better. The former depends on data and infrastructure, the latter on how that information is framed and interpreted. In continuously evolving systems, data alone cannot provide stability—they can only reflect instability with greater precision.

Maintenance as Resilience: For decades, the role of maintenance was clear: prevent failure, reduce downtime, and optimize cost within stable operating conditions. But when those conditions change, the problem itself changes. In uncertain environments, the question is no longer simply how to avoid failure, but how to continue operating when it occurs in unexpected circumstances. The focus moves beyond prevention toward the system’s ability to absorb disruption, adapt, and recover without losing coherence—what we now understand as resilience.

A resilient system is not one that never fails, but one that does not collapse when it does. It continues to function, even under degraded conditions, without crossing into instability. The distinction reshapes the role of maintenance from doing the right things under expected conditions to doing the right things when those conditions no longer hold. Resilience introduces a temporal dimension, where decisions are not only about preventing failure, but also about how the system responds over time.

When dependencies fail or conditions shift, the question becomes whether the system can adapt without triggering further disruption. This requires flexibility—operational, organizational, and technical. Maintenance, in this sense, extends beyond assets. It becomes a capability that supports the organization’s ability to respond under uncertainty. Beyond reliability, decisions about maintenance influence continuity and performance over time.

Systems optimized purely for efficiency may perform well under stable conditions, yet become fragile when conditions change. Resilience acts as a counterbalance, ensuring adaptability in the face of uncertainty. The challenge is to design systems that are both competitive and capable of enduring disruption.

A New Logic: If maintenance is to support resilience, the way risk is understood must also evolve. Not by discarding existing principles, but by extending them beyond the limits for which they were designed. Probability and consequence remain essential, but they no longer capture the full dynamics of how disruption unfolds. What has been missing is not another variable, but a recognition that the significance of failure depends as much on the state of the system as on the failure itself. The same event does not carry the same weight under different conditions. At times, the system can absorb disturbance; at other times, even minor deviations can trigger disproportionate effects. Therefore, risk can no longer be treated as a fixed attribute. It is a condition that evolves with the system, reflecting not only what might happen, but also how prepared the system is to respond. A likely failure may represent little risk if the system is resilient, while a rare event may become critical if exposure is high.

Admittedly, this perspective makes decision-making more demanding. It requires continuous interpretation, the ability to reassess assumptions, and the integration of information that does not fit neatly into predefined categories. Maintenance strategies can no longer be static; they must adapt as the system evolves. From a practical standpoint, it is not enough to ask how likely a failure is, or how severe its consequences might be in general terms. The relevant question becomes: what does this failure mean for the system, here and now?

Answering this requires more than data. It requires a framework that integrates context with analysis, bridging the gap between technical knowledge and operational reality. This is not a departure from established practices, but a reorientation. Reliability and condition monitoring remain essential, but their value depends on how they are connected and interpreted. Risk is no longer static, but dynamic—continuously shaped by the system it describes. In this connection between what the system is and what it is becoming, maintenance begins to act not as a technical function, but as a strategic capability.

The Real Failure: The world maintenance was designed for has quietly changed. The principles that once provided clarity remain valid, but the system they describe no longer behaves in the same way. Its boundaries have expanded, its dependencies have multiplied, and its behavior has become less predictable. In this landscape, the greatest risk is not failure itself, but the persistence of assumptions that no longer reflect reality. Systems are still managed as if conditions were stable, even as they continue to evolve. The meaning of failure shifts with the system, often without being fully recognized.

The more precise our tools become, the stronger the belief that uncertainty is under control. Yet the system reveals new forms of unpredictability—not because it is less understood, but because it is more interconnected and exposed to forces beyond its immediate structure. Control must be reconsidered, not as something that can be fully achieved, but as something that must be continuously negotiated. Maintenance is no longer about preserving stability, but about enabling the system to navigate change without breaking. This shift extends beyond the technical domain. It affects how decisions are made, how risk is perceived, and how efficiency is balanced with adaptability.

Failure itself does not disappear. What changes is its significance. The focus moves from the event to the system’s ability to absorb and respond to it. The real failure is not when an asset stops working, but when the system cannot respond in time—when the gap between expectation and reality becomes too wide to manage. This does not provide certainty, but a different perspective: one that accepts that systems evolve, risk is contextual, and control is always partial. Perhaps that is where the discipline must now position itself—not in the confidence of having mastered the system, but in the awareness of how easily that confidence can be misplaced. The difference is whether we question it in time—or only once it breaks. That silence is still there—just harder to recognize.

Text: Prof. Diego Galar
Photo: shutterstock