Using ChatGPT to Supercharge RCM

Rethinking RCM: Fast-Tracking Strategy with AI

Reliability-Centered Maintenance (RCM) is powerful—but it’s often labelled as slow, resource-intensive, and suitable only for high-criticality assets. Like many in the field, I used to believe that. But after spending time with ChatGPT, my perspective has shifted. What started as curiosity has evolved into a practical toolset for faster, more structured maintenance thinking.

Stage One: Getting Comfortable with the Tool

Initially, I used ChatGPT for simple copywriting tasks—summarising or expanding on things I’d already written. Useful? Yes. Groundbreaking? Not really.

Then I tried it for meeting prep:

"I’ve got a sales meeting with customer X tomorrow. How does my value proposition Y align with their corporate goals?"

Helpful for speed, but still surface-level.

Stage Two: Real Technical Questions

It started to add more value when I asked deeper, open-ended questions:

"Customer X is locked into time-based maintenance. Can you give me some examples from their asset base where that approach might be value destroying?"
"Can you put together a balanced maintenance scorecard for a beverage company?"

These prompts pulled together structured ideas I could work with, saving me time combing through textbooks or standards.

From Q&A to RCM Strategy

Things really changed when I used ChatGPT like a colleague, iterating on complex, technical prompts:

• "I'm working with a beverage business with a reactive maintenance approach. Their Kister tray packer has frequent failures—likely due to poor corrugate storage. What would you recommend?"

• "Can we expand that, especially around changeovers?"

• "What low-cost tools would you recommend for improving changeovers via autonomous maintenance?"

These conversations produced grounded, actionable advice that aligned well with lean and reliability best practices.

A Real-World Example: Building RCM for a KUKA Robot Arm

To really test this, I worked with a colleague on a KUKA robotic arm used for palletising. I wasn’t familiar with the asset or the sector—but we structured an RCM study in minutes.

Here’s what we outlined:

• Function: Automatically and accurately palletise product at the end of the production line.

• Functional Failure: Inability to complete palletising operations to specification.

• Failure Modes: Joint motor wear, encoder misalignment, end-effector damage, software/hardware communication faults.

• Failure Effects: Incomplete or incorrect stacking, cycle time delays, line stoppage.

• Consequences: Reduced throughput, increased manual intervention, potential product damage.

• Proposed Actions: Torque trend monitoring for joints, regular alignment checks for encoders, visual inspection of grippers, and communication diagnostics for control interfaces.

• Recommended Condition Monitoring Tasks: Motor torque trend analysis, encoder alignment verification on a weekly basis, and thermographic inspection of the control cabinet.

A few more targeted prompts helped us build a complete table detailing the full FMEA—ready for SME review. In under an hour, we had a credible RCM plan that could be integrated into a broader reliability program.

Why This Approach Works

RCM doesn’t need to be limited to high-criticality, fleet-style assets. With ChatGPT, we can:

• Rapidly structure the logic for new or unfamiliar equipment

• Let SMEs focus on reviewing and refining—not starting from scratch

• Accelerate time to implementation

Of course, ChatGPT doesn’t replace field knowledge or data analysis. But it gets you 80–90% of the way—fast.

Try This: A 5-Step RCM Template with ChatGPT

1. Set the scene – Share asset details: function, environment, history, and criticality.

2. Build the basics – Ask for functions, failures, failure modes, effects, and consequences.

3. Interrogate the output – Refine anything vague. Add what you know.

4. Define your strategy – Request condition monitoring, default actions, and scheduled tasks.

5. Prepare for deployment – Format outputs into something maintainers and operators can act on.

Final Thought

If you’re sceptical—as I was—spend a couple of hours with an asset you know well. Test ChatGPT’s thinking, layer in your own expertise, and see how quickly you can move from idea to actionable strategy.

You might find that RCM isn’t as out of reach—or as slow—as it used to be.