Industrial plants are redefining maintenance, reliability and digital modernization as they respond to workforce shortages, aging equipment, energy pressures and the growing need for smarter air compressor operations.
Maintenance insights
- Industrial plants are rethinking maintenance as they balance predictive strategies with budget realities, workforce shortages and the growing pressure to reduce energy consumption.
- Suppliers are responding by developing maintenance-focused solutions that combine remote monitoring, retrofit controls, asset management support and OEM expertise to improve reliability, efficiency and decision-making.
In industrial environments, maintenance strategy can determine whether an air compressor remains a reliable asset or becomes a costly source of downtime and inefficiency. As plants face mounting pressure to modernize with fewer resources, this Q&A explores how maintenance priorities are reshaping operations, technology adoption and supplier support.
The expert: Thomas Nist is a Product Manager for Aftermarket and Controls at FS-Elliott, where he focuses on advancing intelligent control solutions and aftermarket programs for centrifugal air compressors. He brings extensive experience in global product management from roles at Kennametal and BlackHawk Industrial, where he managed industrial portfolios and launched new technologies that drive performance and reliability.
What are the biggest shifts you see in the needs and priorities of industrial plants and how are those shifts influencing the way suppliers develop new products and services?
The most significant shift we’ve seen is the transition from reactive to predictive operations, however this transition does not apply to plant operations.
A meaningful segment of the market has moved in the opposite direction, toward a “run to fail” mode. Plants facing budget constraints, workforce shortages and the loss of experienced maintenance personnel are increasingly operating equipment until it breaks rather than investing in proactive care. As seasoned technicians and reliability engineers retire, the institutional knowledge required to interpret early warning signs leaves with them and that gap is genuinely difficult to fill. When you don’t have the people or the expertise, proactive maintenance can feel like a luxury rather than a discipline.
A second major shift is the energy imperative. With energy costs and sustainability targets both rising, compressed air systems are often one of the largest energy consumers in a facility and bring greater scrutiny than ever. Customers are asking how we can help them run more efficiently by reducing their energy consumption. That has an impact on how we approach both control upgrades and service recommendations.
For suppliers, both dynamics carry real implications. The run-to-fail reality is partly a knowledge problem and remote monitoring, condition-based service agreements and original equipment manufacturer (OEM)-backed asset management programs are designed to close that gap, giving plants the visibility and interpretation they lack internally, packaged in a way that fits constrained budgets. For customers investing in predictive operations, the expectation has shifted well beyond traditional break-fix service. They want their compressor fleet treated as a managed asset, with continuous health monitoring, performance trending and data-driven service recommendations. Both ends of that spectrum are actively shaping how we develop and deliver products and services today.
When manufacturers evaluate new solutions for plant operations, what problems are they most urgently trying to solve right now?
The most urgent problems center around aging equipment with increased difficulty obtaining replacement parts, difficulty finding and retaining skilled maintenance technicians and pressure to reduce energy consumption without sacrificing reliability.
On the equipment side, many plants are running compressors that are 20 to 30 years old with legacy controls that lack connectivity, diagnostics or any integration with modern plant systems. These machines still work, but they are islands of data that the plant cannot see or act on. Our controls retrofit program addresses this directly by giving legacy compressors the visibility and intelligence of a modern asset without a full capital replacement.
The workforce issue compounds this. When experienced maintenance staff retire, institutional knowledge about how to read a machine goes with them. Customers increasingly need their equipment supplier to be a more active partner in identifying and developing issues, not just a parts vendor.
What common challenges do plant engineers and managers face when trying to modernize operations while still maintaining production uptime?
The core challenge is that modernization almost always requires intervention on equipment that plants cannot afford to shut down. For critical compressor infrastructure, there is often very little tolerance for planned outages, let alone unplanned ones. Engineers are asked to improve a system they cannot afford to stop.
We address this in a few ways. First, many of our control upgrade packages are designed to be installed during scheduled maintenance windows, minimizing incremental downtime.
Second, we work closely with customer engineering teams in the planning phase to anticipate integration challenges with existing plant control systems, electrical infrastructure and auxiliary support systems. The surprises that typically extend a shutdown are largely preventable with thorough engineering upfront.
What mistakes do plants most often make when evaluating or implementing new solutions and how can they avoid them?
The most common mistake we see is evaluating a solution based primarily on acquisition cost without a full picture of the total cost of ownership. A lower-cost aftermarket part or third-party service contract can appear attractive but may carry hidden costs in the form of reduced equipment life, voided OEM warranties or performance degradation that is difficult to attribute until significant damage has occurred. Centrifugal compressors are precision machines and the tolerance for non-OEM components in critical rotating systems is much lower than customers sometimes assume.
A second frequent mistake is underinvesting in the transition itself. Plants sometimes purchase a new control or monitoring system and then underestimate the internal effort required to integrate it, train staff and establish the processes needed to act on the data it produces. Technology that generates information no one has time to review or the context to interpret delivers very little value.
What factors should plant engineers and managers consider when deciding where artificial intelligence (AI) can deliver real operational value in a manufacturing facility?
Data quality and continuity are foundational. AI tools are only as reliable as the data they learn from. For compressor systems, this means having properly instrumented machines with reliable sensors, consistent data collection over time and enough historical context to distinguish normal variation from meaningful trends. Compressors that have been poorly instrumented or that have inconsistent data collection will not yield reliable predictions, regardless of the model’s sophistication.
Equally important is the cost of being wrong. The right application for AI-assisted alerting is one where a false positive has a manageable cost and a missed detection has a serious one. That asymmetry makes rotating equipment a natural fit. The downside of an undetected bearing fault is catastrophic; the cost of a maintenance check prompted by an alert that turns out to be a sensor artifact is modest.
Beyond data, you still need experienced people in the loop. AI can flag something unusual, but it takes a knowledgeable technician or engineer to determine whether that flag means the machine is developing a real problem, something in the process has simply changed or a sensor is giving a bad reading.
That is also where OEM involvement becomes especially valuable. Equipment manufacturers bring decades of application knowledge, aerodynamic design expertise and a deep understanding of how rotating equipment behaves across different operating conditions. When AI solutions are developed alongside the engineers who design and support the equipment itself, the models can incorporate a much more accurate understanding of machine behavior, failure modes, performance expectations and operational context.
The most effective AI-driven maintenance strategies combine high-quality operational data, experienced plant personnel and OEM engineering expertise.
What does digital transformation mean in practical terms for industrial plants, beyond the buzzwords?
Digital transformation has a straightforward operational meaning: it is the ability to see what their equipment is doing, understand what that data means and act on it faster and more confidently than they could before.
In the context of centrifugal compressor systems, this plays out across several dimensions. Connectivity means getting real-time performance data from machines that previously generated no digital information at all. Analytics means turning that data into actionable insight rather than just another dashboard to monitor. And integration means making compressor health and performance data available to the broader plant systems so that operations, maintenance and engineering teams are working from the same picture.
For our aftermarket business, this has changed the nature of the supplier relationship. We are no longer simply a parts and service provider. We are increasingly a partner in compressor fleet management, working with our channel partners to help customers track asset health across multiple machines and sites, identify the right timing for overhauls and build the operating history that supports better capital planning.
What foundational issues should plants address before pursuing broader digital transformation efforts?
Instrumentation is the foundation on which everything else depends. You cannot monitor what you cannot measure and you cannot predict what you do not have history on. Before investing in analytics or AI platforms, plants should audit whether their critical assets are properly instrumented with reliable, calibrated sensors and whether that data is being collected and stored with sufficient security and continuity.
Data governance is closely related. Knowing who owns compressor data, where it lives and who has access to it has become increasingly important as more operational data moves through cloud platforms and as information technology (IT) and operational technology systems converge. Plants that have not addressed data architecture and security before expanding connectivity often find themselves revisiting those decisions at additional cost.
Process readiness rounds out the picture. Digital tools generate information, but changing operational behavior requires clear processes. Who reviews alerts, who decides when to act and how findings get translated into work orders need to be answered first. Plants that answer those questions and establish the proper processes before deploying new technology extract far more value from it.
How important is cross-functional collaboration among engineering, operations, maintenance, safety and IT teams and where do you see the biggest communication gaps?
Cross-functional collaboration is not optional in a successful modernization effort, rather it is the precondition for it. A controls upgrade touches engineering, operations, maintenance, safety and increasingly IT and if those groups are not aligned on the problem being solved and the outcomes being measured, even a technically sound implementation can stall or fail to deliver lasting value.
In many facilities, one of the less-discussed challenges is the wide range of technical experience and digital familiarity that now exists across the workforce. Experienced engineers and technicians often develop their expertise through years of direct interaction with equipment, troubleshooting and observing operations. That operational knowledge is difficult to replace and remains essential in modern industrial environments.
At the same time, newer generations entering the workforce are often highly comfortable with digital tools, analytics platforms and connected technologies. Facilities that navigate this transition successfully tend to treat it as a two-way knowledge transfer opportunity rather than a technology divide.
The experienced workforce’s pattern recognition is exactly what makes predictive analytics useful rather than noisy and that context needs to be built into how alerts are designed and interpreted. At the same time, digitally fluent personnel can help accelerate adoption by demonstrating how monitoring technologies support day-to-day maintenance decisions, troubleshooting and operational planning. Suppliers have a role here too. Monitoring platforms must support a broad range of users across maintenance, operations and engineering teams. The most effective systems are not simply data-rich; they are intuitive, practical and capable of translating operational data into actionable insights across the entire organization.
