Manufacturers can reduce downtime and maintenance costs in legacy automation systems by implementing practical predictive maintenance focused on critical assets and their existing warning signs, rather than waiting for complete modernization or full equipment replacement.
- Predictive maintenance can increase asset availability by 5-15% and reduce maintenance costs by 18-25% according to McKinsey research.
- Start with critical assets that cause the most operational pain when they fail, such as drives, motors, and motion components.
- Document existing warning signs like repeat faults, thermal issues, alarm history, and technician observations to trigger earlier interventions.
- A phased strategy focused on one production area or asset class typically works better than attempting plant-wide modernization at once.
- Capture and formalize technician knowledge into repeatable procedures to ensure consistent response to pre-failure symptoms across shifts and teams.
Manufacturers are under pressure to improve uptime, control maintenance costs, and increase output, even though many facilities still depend on aging drives, motors, HMIs, PLCs, and other automation hardware that cannot be replaced all at once. At the same time, smart manufacturing has become a major competitive priority.
According to Deloitte’s 2025 Smart Manufacturing and Operations Survey, 92% of surveyed manufacturers believe smart manufacturing will be the main driver of competitiveness over the next three years, even as many organizations still lag in maintenance and workforce maturity.
That creates a practical challenge for real-world plants. Many operations are not starting from a clean slate with fully connected systems and modern equipment across every line. Instead, they are running mixed environments where older automation platforms still support critical production. In that setting, predictive maintenance becomes less about chasing a perfect digital transformation and more about building a smarter maintenance strategy around the equipment that already exists.
For facilities with legacy automation, predictive maintenance is often most effective when it is realistic, phased, and closely tied to actual failure patterns rather than buzzwords. As McKinsey’s work on digitally enabled reliability notes, effective predictive maintenance programs can increase asset availability by 5% to 15% and reduce maintenance costs by 18% to 25%.
Why legacy automation changes the maintenance conversation
Many discussions about predictive maintenance assume every machine produces clean data, every controller is easy to integrate, and every asset can connect to an analytics platform. Most plants do not operate in that kind of environment. Legacy systems are often kept in service because they are proven, production-critical, and deeply integrated into the process. Replacing them can involve engineering time, retraining, unplanned complexity, and capital spending that many organizations would rather defer.
That reality does not make predictive maintenance irrelevant. It simply changes how it should be applied. Instead of waiting to modernize every asset, maintenance teams can start with the data and experience they already have. That may include alarm history, repeated fault patterns, thermal issues, unplanned stoppage logs, maintenance records, technician observations, repair frequency, and spare parts usage. McKinsey’s guidance on predictive maintenance at scale makes a similar point: successful programs are built asset by asset, based on value and usable signals, rather than treating every machine the same way.
In other words, a plant does not need a perfect Industry 4.0 environment to act earlier on failure risk. It needs a reliable way to recognize which assets fail most often, which symptoms tend to appear before failure, and which interventions prevent those failures from becoming full production events.
Predictive maintenance works better when it starts with critical assets
One of the most common mistakes in maintenance improvement is trying to monitor everything at once. A better approach is to start with the assets that cause the most operational pain when they fail. In many manufacturing environments, that means drives, motors, power supplies, motion components, and controller-dependent systems that can shut down an entire machine or line.
This more selective model aligns with how successful maintenance programs are usually built. Rather than starting with a plant-wide software rollout, teams often get better results by ranking equipment according to downtime impact, failure frequency, replacement difficulty, and service lead time. Then they identify early warning signs and define what should happen when those signs appear.
That process matters because predictive maintenance's value isn't just in collecting signals. It is in turning those signals into earlier action. A recurring overload warning, abnormal cabinet heat, repeated communication dropout, rising restart frequency, or a pattern of intermittent servo behavior should not be treated as background noise forever. Those patterns can help maintenance teams schedule an inspection, check cooling paths, verify electrical connections, back up parameters, secure a repair option, or source replacement components before a line-down event occurs.
That same practical logic appears in McKinsey’s analytics-based maintenance strategy research, which emphasizes combining historical data, logs, and service history to improve maintenance decisions instead of relying on blanket interventions.
What does predictive maintenance look like in a real plant?
On paper, predictive maintenance can sound like a software strategy. On the plant floor, it usually looks much more practical. It means the team notices patterns sooner and responds before production is affected.
For example, a servo drive that starts generating repeat warnings under peak load is sending a message, even if it has not failed yet. A motor that runs hotter than normal under specific operating conditions is sending a message. A legacy control system that requires more resets than it did six months ago is sending a message. Predictive maintenance becomes useful when you document those signals, compare them, and link them to specific actions.
That action does not have to be complex. It may involve scheduled thermal inspections, vibration checks on select assets, fault code reviews, connection checks, fan and cooling inspections, controlled replacement of wear-prone components, or sending a unit for repair before it fails in production. In a mixed automation environment, even a modest improvement in intervention timing can help avoid expensive unplanned downtime.
This is especially important when older but critical hardware remains in service because replacement cycles are slow or supply conditions are uncertain. Deloitte’s 2025 manufacturing research reinforces that manufacturers are modernizing under real operational constraints, and that progress depends as much on practical execution as on technology investment.
The workforce side is just as important as the technology
Predictive maintenance programs often succeed or fail based on how a plant uses internal knowledge. Many facilities already have technicians who understand the warning signs of specific assets long before those signs show up on a dashboard. One technician knows that a certain drive family becomes unstable after a repeat temperature alarm. Another knows that a recurring fault on a motion system usually comes from contamination, poor airflow, or a degrading cable rather than the first alarm shown on the screen.
The problem is that this knowledge often remains informal. If it is not recorded, shared, and turned into repeatable procedures, the plant keeps relearning the same lessons. This becomes even riskier as workforce expectations shift. The World Economic Forum’s Future of Jobs Report 2025 found that employers expect 39% of workers’ core skills to change by 2030, highlighting how quickly workforce requirements are evolving.
For maintenance leaders, that means predictive maintenance is not only a data issue. It is also a knowledge capture issue. Plants need ways to document recurring failures, record pre-failure symptoms, connect actions to outcomes, and make those insights usable across shifts and teams. Even a simple internal playbook built around repeat faults and known failure modes can improve response quality.
That point aligns with Deloitte’s broader view of manufacturing maturity, which shows that workforce readiness and maintenance capability still trail the broader push toward smart operations.
A phased strategy usually works better than a full overhaul
For most facilities, the best predictive maintenance program is not the most advanced one. It is the one the plant can sustain.
A phased strategy often works best. Start with one production area or one asset class. Track a small number of meaningful indicators such as repeat faults, downtime frequency, heat trends, emergency interventions, mean time between failures, and maintenance events tied to the same equipment. Use that information to define trigger points for action.
From there, a plant can add tools that fit its environment. That may include condition monitoring on selected assets, thermal imaging during scheduled downtime, improved alarm review processes, better maintenance logs, or tighter coordination between production records and maintenance records. More advanced analytics may come later, but early returns usually come from better prioritization and earlier intervention.
As McKinsey explains in its predictive maintenance scaling analysis, not every asset is equally suitable for predictive methods, and economic value depends on asset criticality, the cost of failure, and the usefulness of available signals.
Why this matters now
The argument for practical predictive maintenance is becoming stronger as manufacturers balance modernization goals with real operating limits. Deloitte’s recent survey makes clear that manufacturers increasingly view smart operations as central to competitiveness, but they are still working through workforce and maintenance maturity challenges.
That makes selective modernization a more practical path for many facilities. A plant does not have to choose between doing nothing and replacing everything. It can improve uptime by identifying critical legacy assets, documenting the warning signs those assets already produce, and acting earlier when those signs appear. The result is a maintenance strategy that supports modernization without requiring a disruptive all-at-once transformation.
Final takeaway
Predictive maintenance for legacy automation is not about pretending older equipment behaves like brand-new connected hardware. It is about building a smarter response system around the real equipment environment in the plant.
When manufacturers begin with their most critical assets, use the warning signs already available, and translate technician experience into repeatable action, predictive maintenance becomes practical. That is what makes it valuable. It is not simply a technology initiative. It is an uptime strategy grounded in how manufacturing plants actually operate.
Jesse Walker is an engineering tech for Wake Industrial. As a versatile electro-mechanical technical engineer with a strong foundation in automation design, testing, and optimization of complex systems, Jesse is proficient in automation design and focuses on bridging mechanical functionality and electronic control to deliver innovative, reliable products from concept to production.
