When people talk about AI in manufacturing, they usually mean factories in China or Germany. But there’s a metal fabrication business in Ballarat that’s been quietly using predictive maintenance AI for the past 18 months—and the results are worth sharing.

I visited Westpac Engineering (not affiliated with the bank) last month to learn how a regional manufacturer with about 40 staff implemented technology that’s usually discussed in the context of massive multinationals.

The Problem They Were Solving

“We had a CNC machine go down unexpectedly in 2022,” explained operations manager Tom Briggs. “It took three weeks to get parts from Germany. We lost a contract because of it.”

Unplanned downtime is devastating for manufacturers. You’re paying staff who can’t work, missing delivery deadlines, and potentially losing customers permanently.

Traditional maintenance approaches are either reactive (fix it when it breaks) or scheduled (service everything every X months). Neither is ideal—reactive means unexpected failures, while scheduled maintenance often services machines that don’t need it yet.

Enter Predictive Maintenance

Predictive maintenance uses sensors and AI to monitor equipment continuously, detecting early warning signs of failure before they become serious.

For Westpac, this meant installing vibration sensors, temperature monitors, and power consumption trackers on their critical machines. The data feeds into software that learns normal operating patterns and flags anomalies.

“The system told us our main lathe bearing was showing unusual vibration patterns,” Tom said. “We scheduled replacement during our Christmas shutdown. Two months later, the manufacturer issued a recall on that exact bearing model—they were failing across the industry.”

The Implementation Journey

Here’s where it gets interesting for other regional manufacturers considering this.

Phase 1: Starting small

They didn’t sensor everything at once. They identified their three most critical machines—the ones that would hurt most if they failed—and started there. Total initial investment was around $15,000 including sensors, installation, and software subscription.

Phase 2: Learning the system

The first six months generated a lot of false positives. The AI was learning, and so was the team. They needed to understand which alerts mattered and which were noise.

“We probably over-reacted to alerts in the early days,” Tom admitted. “But that’s better than ignoring a real warning.”

Phase 3: Expanding coverage

After proving the concept, they rolled out to eight more machines. They also integrated the system with their maintenance scheduling software.

The Results

After 18 months:

• Zero unplanned downtime on monitored equipment
• 22% reduction in total maintenance costs (less unnecessary scheduled maintenance)
• Faster parts ordering—they often know they’ll need parts weeks before the machine fails
• Better insurance rates—their insurer offered a discount for proactive maintenance

“The ROI was probably 12 months,” Tom estimated. “And that’s being conservative.”

What Made It Work

Several factors contributed to success:

Leadership buy-in. The owner understood this was strategic, not just a tech experiment.

Staff involvement. Maintenance team members were included from the start. They understood the machines better than any software, and their input made the system more accurate.

Realistic expectations. They expected a learning curve and planned for it.

Local support. They worked with an integrator based in Ballarat who could provide on-site help when needed.

Lessons for Other Regional Manufacturers

Tom’s advice for others considering predictive maintenance:

1. Start with critical equipment. Don’t try to sensor everything immediately.

2. Budget for the learning period. First six months will be bumpy.

3. Involve your maintenance people. Their knowledge is invaluable.

4. Look for Australian-supported solutions. Having local support matters when things go wrong.

5. Consider industry-specific options. Some predictive maintenance solutions are designed for specific equipment types.

The Bigger Picture

What impressed me most about Westpac Engineering was their mindset. They’re a regional manufacturer competing with larger businesses, and they’ve decided that technology is part of how they stay competitive.

“We can’t compete on scale,” Tom said. “But we can compete on reliability, quality, and being smart about how we operate.”

That’s a philosophy more regional businesses could adopt. Technology isn’t just for the big players—it’s increasingly accessible to anyone willing to learn. Business Victoria offers grants for manufacturers investing in technology, and Regional Development Victoria supports regional business modernisation.

If you’re a regional manufacturer exploring predictive maintenance or other AI applications, specialists in this space like Team400 can help regional businesses implement practical solutions that work with existing systems and local constraints.