Turning Data Into Decisions: Quantifying the Value of Condition-Based Monitoring

Insights from Gary Wood’s session “Turning Data into Decisions: Quantifying the Value of Condition-Based Monitoring” at Xcelerate 25.

Condition-based monitoring (CBM) has long promised to revolutionize maintenance, but for many organizations, it remains a technical function rather than a strategic advantage. The difference often lies not in technology, but in translation: how effectively leaders connect machine data to measurable business outcomes.

“Every organization talks about uptime and efficiency,” says Gary Wood, Customer Success Manager at Fluke. “But until you can quantify it — until you can show in dollars what condition-based monitoring saves — it’s just talk.

Wood’s perspective is grounded in decades of experience divided between manufacturing floors and consulting across industries. His argument is simple but powerful: the credibility of a reliability program depends on its ability to express performance in financial and operational terms that executives understand. The challenge, as Wood sees it, is that most companies have the data — they just haven’t learned how to turn it into proof.

From Maintenance Data to Business Intelligence

Turning data into proof starts with knowing what matters most. For Wood, that means identifying the assets that keep production moving — and ignoring the noise. “Before you wire up a single motor, you need to know which ones stop the line,” he says. “That’s where an asset criticality assessment comes in.”

An asset criticality assessment ranks equipment by its impact on production, cost, and safety — pinpointing where downtime hurts most. It’s the foundation of any CBM strategy worthy of investment. Once assets are clearly identified and ranked, condition data gains meaning, tying directly to the business metrics that executives care about.

“Condition monitoring doesn’t create value by itself,” Wood says. “Interpretation does.” The organizations that get it right treat CBM as a diagnostic engine — one that quantifies performance continuously and connects reliability to revenue.

Wood shares an example of a tire press at Goodyear to make the point. When a press motor fails without CBM, downtime runs about six and a half hours, costing roughly $10,000 in lost production, labor, and parts. With CBM, vibration alerts flag the problem early, letting maintenance pre-stage a replacement motor and schedule the swap. Downtime drops to 90 minutes, and total cost falls to $2,500. That single event saves $7,500. Scale it across a dozen presses on 12-hour shifts, and the avoided losses top $75,000 in a day. CBM turns maintenance insight into a business case in motion.

Redefining Preventive Maintenance

Those savings don’t just show up in the balance sheet — they reshape how maintenance happens. Once an organization can prove the value of CBM, the next step is using that insight to refine preventive maintenance itself.

Traditional PM programs rely on fixed intervals — quarterly, semiannual, annual — regardless of asset performance. CBM breaks that pattern. “When you see the same motor showing vibration issues every two months, why wait three?” Wood says. “And if your 25,000-horsepower compressor looks perfect, why touch it just because the calendar says so?”

CBM turns static schedules into dynamic insights. It gives maintenance teams the evidence to pull a PM forward — or push it back — based on real conditions, not routine. Over time, that flexibility translates into longer asset life, fewer unnecessary interventions, and far less unplanned downtime.

Wood often calls this the bridge between preventive and predictive maintenance — where data drives timing, not habit. The shift also feeds back into financial performance: fewer premature part replacements, fewer labor hours, and a stronger case for ROI every time a CBM alert prevents a failure.

The Cultural Equation

Refining maintenance strategy is one thing; sustaining it is another. Even the most advanced CBM system can fall flat if the people using it don’t believe in it. “You can have the best software in the world,” Wood says, “but if your team doesn’t trust it, they won’t use it.”

That trust starts on the shop floor. Organizations that see lasting results make CBM a shared discipline, not a top-down directive. They bring technicians into the process early on — helping set alert thresholds, define workflows, and interpret data trends. When teams have a hand in shaping the system, they take ownership of it.

The difference appears quickly. Alerts get attention sooner. Data stays cleaner. Insights actually drive action. Over time, that engagement feeds back into reliability, turning a technology initiative into a culture of accountability.

In the end, culture carries what technology starts. Without user buy-in, even the smartest system is just another dashboard.

Building a Financial Case for Reliability

When culture and process align, CBM moves from an engineering project to an executive discussion. The task for maintenance leaders is clear: translate technical metrics into business language.

The framework stays simple:
Downtime avoided: hours × production value per hour
Maintenance cost reduction: planned versus unplanned work
Asset life extension: deferred replacement cost
Parts and logistics savings: fewer expedited orders

Tracked consistently, these numbers become KPIs not just for maintenance, but for the entire organization. They quantify resilience — the ability to sustain production, manage risk, and protect profitability. The broader Fluke ecosystem — including Azima DLI AI-driven diagnostics — adds another layer of intelligence to this process. Machine learning turns vibration data into prescriptive insights, helping teams act before small issues cascade into costly downtime.

“The companies that get this right,” Wood says, “don’t talk about CBM as technology. They talk about it as business strategy.”

The Strategic Takeaway

Condition-based monitoring isn’t a maintenance initiative; it’s an information strategy. It bridges the gap between operational reliability and financial accountability. The organizations that succeed with CBM treat machine data as a management resource, not just a maintenance tool.

Quantification is the bridge. When reliability leaders translate vibration trends into dollar figures and align maintenance decisions with ROI, they shift from being technicians of uptime to architects of value.

Key Insights

• Measure what matters. Translate downtime, parts, and labor into financial terms that leadership understands.
• Let data drive PMs. Replace static schedules with condition-based intervals that reflect actual equipment health.
• Build belief, not just systems. Technicians who help shape CBM processes also help sustain them.
• Make reliability a strategy. The real value of CBM is organizational — creating a culture that connects performance to profit.