When I talk about reactive operations, I’m talking about the fire-fighting mode. Whatever is broken is the priority. For instance, your maintenance team spent yesterday replacing a bearing. Parts that should have shipped today will not ship until Friday.
These are classic examples of reactive operations, and reactive operations are incredibly expensive.
I’ve been thinking about this a lot lately because everyone wants to “do predictive maintenance” but many companies aren’t really ready for it. You can’t just install some AI and suddenly you’re predicting everything. There are several stages you must follow to get there. And trying to skip stages is usually what makes these projects fail.
Stage 1: Firefighting Mode
A lot of companies operate at Stage 1. They let equipment run until it breaks, then everyone scrambles to get things running again. I was talking to an operations manager last month who said his team spends about 60% of its time responding to breakdowns. They’re not preventing anything. They’re just jumping from one crisis to another.
The data situation in these operations? Typically, operators or plant managers can rustle up some manual logs. SCADA alarms might go off after something’s already gone wrong. Often, you can find reports that show you what happened last week, which is useless for preventing anything today.
And downtime costs a fortune. The Aberdeen Group claims it costs about $260,000 per hour for unplanned downtime. I don’t know if that number’s accurate for every industry, but the point is that reacting to failures instead of preventing them bleeds money.
Stage 2: We Schedule Things Now
So, the first step most companies take is just putting equipment maintenance on a schedule. Let’s hange the oil every 2,000 hours. Replace bearings yearly. Whatever.
This prevents a few failures. But it also wastes a lot of money when you are replacing parts that don’t need replacing yet. Meanwhile, other equipment that needs attention just degrades between scheduled checks.
A local food processing company calculated that they were tossing 40% of their components that still had life left in them. Meanwhile, equipment that needed attention was failing between inspections. So, they were spending more on unnecessary maintenance than reactive repairs.
Time-based maintenance is not wrong but incomplete. Because equipment doesn’t fail on schedule. It fails based on how hard you’re running it, what you are processing, temperature, humidity, operator technique. All the stuff that varies day to day.
Stage 3: Monitoring Actual Conditions
This is where real-time operational intelligence starts to matter. You are watching actual equipment conditions instead of just marking calendars. Temperature trending up? Vibration changing? Power draw increasing? These tell you something’s developing before it breaks.
A pharma manufacturer that had implemented AI prediction started monitoring their critical pumps. They found bearing degradation signatures showing up 7-14 days (about 2 weeks) before failure. Not always—AI is not magic—but often enough to prevent three production stoppages in six months, which at their scale is serious money.
You need more infrastructure for that kind of thing than you do for simple preventive maintenance. Sensors, data collection, and someone to actually look at the information. But your ROI shows up quickly, because you’re fixing things based on need rather than arbitrary schedules.
Stage 4: Predicting Failures
Condition monitoring tells you when your equipment is degraded. Predictive analytics tells you, with a certain level of confidence, when it may fail.
That distinction matters more than you would think. An operator notices a bearing running hot. Okay, but does it last another week or fail tomorrow? Predictive models trained on historical patterns can answer that question with surprising accuracy.
The machine learning approach analyzes months or years of operational data, maintenance records, and failure events. It finds clues that humans can’t easily see—combinations of temperature, vibration, and power consumption that happen right before failures.
An automotive supplier trained models on three years of maintenance logs from 47 CNC machines. That supplier achieved 89% accuracy predicting failures 5-10 days out. That’s enough advance notice to plan most interventions during shift changes instead of having to deal with emergency mid-production repairs.
What surprised me most when I first encountered this was that you do not need perfect data. The AI finds patterns in whatever information exists. Messy maintenance logs still work. The data you’re already collecting is good enough.
Stage 5: Telling You What to Do About It
Predictive systems tell you what will happen. Prescriptive systems tell you what to do to keep it from happening. Prescriptive intelligence might say to order a specific bearing (3-day lead time), schedule maintenance during an already planned downtime window (6 days out), and, if necessary, reduce the flow rate temporarily to extend the pump life.
This requires connecting a bunch of systems like maintenance management, inventory, production scheduling, and supplier databases. The intelligence layer considers the predicted failure but also the operational context. What parts do you have? When are maintenance people available? How critical is this equipment to meet your customer commitments right now?
A certain hospital medical technology group put prescriptive logic to work for its medical imaging systems. When it predicted an MRI tube problem, their system then automatically checked parts inventory, generated purchase orders for parts it needed, reviewed its tech schedules, and suggested maintenance timing to minimize patient appointment disruption.
Stage 6: Systems Managing Themselves
The most advanced stage is when you get systems to execute responses automatically within defined parameters. If your equipment performance drops, the system will automatically adjust its process parameters to maintain a defined level of quality until the maintenance team can remedy the situation.
This is not about eliminating human decisions. It’s about automating routine optimizations so people can focus on complex situations that require actual human judgment.
Very few manufacturers operate fully at Stage 6. The ones that do, typically started 3-5 years ago at Stage 3 or 4 and worked their way forward diligently and intentionally.
It’s Not Linear (And That’s Fine)
Different parts of your operation can operate simultaneously at different stages. You might implement predictive monitoring on critical equipment while you let other equipment run reactively. And that’s the smart move.
That precision machining shop I mentioned earlier started with condition monitoring on six high-impact CNC machines. Once they proved the value of condition monitoring there, they moved to implement predictive monitoring on those same machines. They moved forward incrementally.
Three years later their critical equipment is at Stage 4 (predictive); their standard production system is at Stage 3 (condition-based), and their facilities systems are still at Stage 2 (preventive). This mixed approach works well because they prioritized impact over comprehensive transformation.
Where Are You Actually?
Most operations overestimate their maturity. Managers say, “we do predictive maintenance” when they mean “we monitor some conditions sometimes.”
Here’s a simple but honest assessment rule-of-thumb:
- You’re at Stage 1 if equipment failures still surprise you, and most maintenance is reactive.
- You’re at Stage 2 if you follow schedules, but those schedules don’t adjust for actual equipment conditions.
- At Stage 3 you’re monitoring equipment and triggering maintenance when degradation hits a certain, defined point, but you’re still reacting to current conditions rather than predicting future failures.
- At Stage 4, your models forecast failures days or weeks ahead with measurable accuracy; not necessarily 100% obviously but consistent enough to base good decisions on.
- At Stage 5, the system recommends specific actions by considering your full operational context.
- And at Stage 6, pre-defined and approved actions execute automatically within defined limits.
Most manufacturers I talk to are operating between Stages 1 and 2. Some have a few small systems operating at Stage 3. But very few have reached Stage 4 consistently.
Why Mature Operations Actually Win
Companies operating at Stage 3 or higher typically report 30-50% reductions in unplanned downtime, 20-25% decreases in maintenance costs, and 15-20% improvements in OEE.
More importantly, they decrease the amount of attention spent on firefighting. When you shift 60% of your maintenance activity from reactive repairs to planned interventions, your operations become much more predictable. Predictability can compound into a competitive advantage. If you can commit to shorter lead times because your production flow is more predictable than the competition, you will take business from competitors who still work around unexpected downtime.
Actually Starting
If you’re ready to move beyond firefighting, an honest assessment comes first. What stage are you actually at? What data do you collect currently? What problems cost the most when they occur?
Take one stage at a time for your highest-impact equipment or processes. Prove the value of condition monitoring before expanding the scope.
That pharma manufacturer didn’t implement predictive maintenance across their entire facility. Instead, they started with four critical pumps that historically had caused expensive disruptions. Once they were successful with those pumps, they started expanding to other systems.
That’s how operational maturity actually moves ahead. Deliberately and with measurable results at each stage. There’s no such thing as complete overnight transformation.
About the Author
Kevin Ahlvin, VP of Marketing
Kevin Ahlvin is a strategic growth leader focused on translating complex technology into clear market value. At Bridgera, he drives brand positioning and demand generation across AI consulting, specialized talent, and proprietary data and agent platforms. Kevin is passionate about making artificial intelligence practical and accessible, helping organizations move beyond experimentation to measurable impact. He believes the companies that win in the AI era will be those that combine technical innovation with clarity, education, and purposeful execution.
