Why Most Condition-Based Maintenance Programs Fail — And How to Make Yours Deliver ROI Fast

Aug 14, 2025

Condition Based Monitoring

Introduction: The Promise and the Reality of CBM

In manufacturing, few maintenance strategies have been hyped more in the past decade than Condition-Based Maintenance (CBM) and its increasingly sophisticated cousin, Predictive Maintenance (PdM).

The value proposition sounds simple and irresistible: install intelligent sensors on your critical assets, use AI-driven analytics to monitor them continuously, and receive early warnings when something is about to fail. With that advance notice, you can schedule the repair for a time that’s less disruptive, avoid unexpected breakdowns, extend asset life, and reduce maintenance costs.

The marketing is compelling — and it’s backed by real technical capability. Modern CBM platforms can detect potential failures well before they occur, sometimes months in advance. They can even suggest likely causes and recommended actions, making it easier for maintenance teams to respond.

But here’s the uncomfortable truth: in the real world, most CBM programs fail to deliver their promised ROI.

If you’ve ever invested in a CBM platform and, months later, found yourself still battling the same breakdowns, hearing the same complaints from production, and facing the same budget pressures, you’re not alone. Across industries — and especially in agri-food manufacturing, where margins are tight and downtime costs are high — many CBM projects stall, with their potential value left on the table.

The cause of failure is rarely the technology itself. In most cases, the sensors are working, the data is flowing, and the algorithms are making accurate predictions. The problem is something more fundamental: the organisation isn’t able to act on the insights in time to make a difference.

This article will show you why that happens, what you can do about it, and how sites are using Factory AI’s approach to turn CBM alerts into hundreds of thousands of dollars in avoided downtime.

The Problem: Why Good CBM Technology Still Fails

The P-F Curve: CBM’s Core Value

To understand why CBM is so important — and why it often underdelivers — we need to revisit one of the foundational concepts in reliability engineering: the P-F curve.

The P-F curve is a conceptual diagram showing the degradation of an asset over time. It has two key points:

P — the Potential Failure Point, when a fault becomes detectable through condition monitoring.
F — the Functional Failure Point, when the equipment can no longer perform to specification.

The time between P and F — the P-F interval — is your window of opportunity to detect, diagnose, and address the fault before it causes a breakdown.

For example:

A bearing in a conveyor drive might start to show abnormal vibration 12 weeks before it seizes.
A gearbox might start to run hotter than normal six weeks before a tooth breaks.

If you can detect these signs at Point P, you can plan the repair, order parts, schedule the work during a planned downtime, and avoid an expensive, disruptive failure.

CBM’s entire purpose is to detect issues as close to Point P as possible, giving you maximum time to respond.

Most Failures Are Not Age-Related

Many preventive maintenance (PM) programs still rely heavily on time-based tasks: “replace this belt every 12 months,” “overhaul this pump every 2 years.” While some failure modes are predictable like this, the majority are not.

Groundbreaking research by Nowlan & Heap — conducted for the airline and military sectors — found that 70–90% of failure modes are not age-related. Instead, they occur randomly or as a result of changing operating conditions.

That means:

Components can fail at any time, regardless of age.
Time-based PM will either miss these failures or result in unnecessary maintenance.
The only reliable way to manage them is through condition monitoring.

This is why CBM is a crucial tool in any modern reliability strategy. Without it, you’ll always be blindsided by unexpected failures.

Credit to Reliability Academy for the great visual (link here : https://reliabilityacademy.com/cbm-fails-without-planning/)

When CBM Goes Wrong

If CBM is so essential, why do so many implementations fail? The short answer: reactive maintenance culture.

Here’s a common scenario we’ve seen play out in plants across Australia and beyond:

Your CBM system detects an abnormal vibration trend on a high-value pump.
It generates an alert and logs a work request in the CMMS.
The planner is already overloaded with urgent breakdowns and deferred PMs.
Operations won’t approve an unscheduled stop because the next planned outage is weeks away.
The job is postponed.
Months later, the pump fails mid-shift, causing hours of unplanned downtime.

This is a textbook case of having the data but not the ability to act.

The failure isn’t with the sensors or the analytics. It’s with the execution process. In a reactive environment, today’s emergencies always take precedence over tomorrow’s problems — even when “tomorrow’s problem” is a predicted $40,000 gearbox failure.

The Hidden Cost of Ignored Alerts

When CBM alerts are ignored or delayed, you get the worst of both worlds:

You’re still experiencing unplanned breakdowns.
You’ve also invested in a CBM platform, sensors, and integration — but aren’t capturing the savings they could deliver.

As one industry report put it:

“Without effective planning and scheduling, CBM is just run-to-failure — only more expensive, because you’re paying for the privilege.”

In other words: technology alone can’t make you reliable. It’s the process that turns detection into value.

Case in Point: What Happens Without Execution Discipline

One of Factory AI’s customers — before working with us — had experienced a particularly telling incident. Their CBM system detected a bearing issue in a key conveyor motor. The alert came with two weeks’ lead time and a clear recommendation to replace the bearing during the next weekend shift.

Unfortunately, a staffing shortage meant the maintenance team prioritised another urgent job. The bearing replacement was pushed back. Two weeks later, the motor failed, taking down the line for eight hours and costing tens of thousands in lost production.

This isn’t an isolated case — it’s a pattern. And it’s why many CBM vendors privately admit that their biggest challenge isn’t detection accuracy, it’s getting customers to act on the data.

The Insight: Planning & Scheduling Is the Missing Link

Why Process Matters More Than Sensors

Across ten respected industry sources — from Reliabilityweb’s 7 Steps to Effective Planning & Scheduling to IDCON’s How Planning & Scheduling and CBM Work Together to the U.S. DoD CBM+ Guidebook — the message is consistent: CBM only delivers ROI when it’s embedded in a disciplined planning and scheduling process.

This makes intuitive sense:

The whole point of CBM is to give you time to plan the work.
If you can’t plan and schedule effectively, that time is wasted.

Planning and scheduling is the bridge between knowing about a problem and fixing it before it fails.

What Best-in-Class Looks Like

In high-performing plants, CBM alerts follow a closed-loop workflow:

Detection — The CBM system detects a deviation in condition (e.g., vibration, temperature, oil quality).
Diagnosis — A reliability engineer reviews the alert, confirms the likely failure mode, and assesses the urgency.
Decision — The job is prioritised based on risk, cost of failure, and the P-F interval.
Planning — The planner ensures all materials, tools, and permits are ready.
Scheduling — The job is slotted into the frozen weekly schedule and agreed with operations.
Execution — The work is carried out as planned.
Feedback — The technician records the cause, corrective action, and any notes, which are fed back into the CBM system to improve future predictions.

Supporting this process are a few non-negotiables:

Protected CBM slots in the weekly schedule — so proactive work doesn’t get bumped by urgent-but-less-critical jobs.
Clear priority codes so critical CBM jobs get the attention they deserve.
Parts kitting to prevent delays once the work starts.
Schedule compliance metrics to hold teams accountable for executing the plan.

The Solution: Building a CBM-Ready Work Environment

The good news is that the CBM failure pattern we explored in Part 1 isn’t inevitable. In fact, once you understand the root cause — a lack of planning and scheduling discipline — the path to success becomes clear.

By focusing first on stabilising your maintenance environment and then embedding CBM into that structure, you can unlock the full value of your investment.

At Factory AI, we use a five-step framework to help our customers do exactly that.

Step 1 — Stand Up Work Management First

Before you add more data into an already overloaded system, you need a reliable work management process.

This starts with Planning and Scheduling as distinct roles and activities:

Planning is about preparing the job: scoping the work, confirming asset details, ordering parts, arranging permits, and specifying the tools and skills needed.
Scheduling is about deciding when the job will be done and making sure the resources are available.

A few non-negotiables:

Dedicated Planner — Someone whose primary job is to prepare work. If your “planner” is constantly pulled onto breakdown jobs, they’re not really planning.
Frozen Weekly Schedule — Agree a week’s work in advance with Operations and Maintenance, and protect it from last-minute changes unless absolutely necessary.
Schedule Compliance Tracking — Measure the percentage of scheduled work actually completed as planned. Best-in-class is >80%.
Weekly Alert Review — Reliability, Planning, and Operations meet weekly to review all CBM alerts and decide which become scheduled jobs.

When this structure is in place, CBM alerts have a clear pathway into execution.

Factory AI's Prevent product can help make it much easier to use a CMMS.

Step 2 — Pick Winnable Assets for CBM

Not all assets are equal when it comes to CBM. Trying to monitor everything from day one can overwhelm your team and dilute the impact. Instead, focus on assets that give you the biggest bang for your buck.

Selection criteria:

High cost of failure — in downtime, scrap, or repair cost.
Detectable failure modes — vibration, temperature, lubrication, or other measurable indicators.
Sufficient P-F interval — enough time between detection and failure to plan and execute the job.
Known troublemakers — assets with a history of unplanned downtime or costly repairs.

Factory AI example: In several pilots, we’ve started with rotating equipment like pumps, gearboxes, and fans. These have clear failure modes (bearing wear, misalignment, lubrication issues) and measurable indicators (vibration, temperature)

Step 3 — Define Triage Rules

Once you’re monitoring the right assets, you need to decide how you’ll handle incoming alerts. Without clear triage rules, alerts risk being treated like “suggestions” — acted on only when convenient.

Key decisions:

Who reviews alerts? In many plants, the Reliability Engineer is the logical owner.
How fast? Set a Service Level Agreement (SLA) for reviewing alerts — e.g., all new alerts reviewed within 24 hours.
How to prioritise? Map alert severity to specific actions:
Red: Immediate action — plan for earliest possible slot, potentially override frozen schedule.
Amber: Plan and schedule within 1–2 weeks.
Green: Monitor trend and plan for next outage.

The goal is consistency: the same type of alert should trigger the same type of response, regardless of who’s on shift.

Step 4 — Close the Loop in the CMMS

Your CMMS is the backbone of your maintenance execution. If CBM alerts don’t integrate cleanly into it, you’ll lose time and context.

Best practices:

Auto-create work orders from accepted CBM alerts, pre-populated with:
Asset ID and location.
Failure mode (if known).
Estimated downtime cost if not addressed.
Recommended actions and required parts.
Link to historical data so planners can see past failures for the same asset.
Require feedback on completion:
Failure codes.
Cause codes.
Technician notes.
Confirmation if the CBM prediction was accurate.

This feedback loop improves both your CBM models and your root cause analysis process.

Step 5 — Measure What Matters

To prove ROI — and keep leadership support — you must track the right metrics. We recommend:

Avoided downtime — hours and dollar value, using historical failure data for baseline.
Planned Work % — aim for >55% of all maintenance hours to be on planned work.
Schedule Compliance — aim for >80% of scheduled work completed as planned.
Alert closure rate — % of CBM alerts that result in completed jobs before failure.

Bringing It to Life: Real-World Examples

Large Biscuit Manufacturing Plant

The Challenge: $12.48M annual downtime cost; frequent undetected failures despite existing maintenance practices.
The Approach: Deployed Factory AI across 200+ assets, with alerts feeding into a protected weekly schedule.
The Result: $260,500 in savings in just 6 months. One notable save was a gearbox lubrication issue detected early — addressed before it caused a $40,000 failure.

Global Dairy Producing Site

The Challenge: Over-maintenance — bearings, belts, and motors replaced too early, costing >$100K/year.
The Approach: Condition monitoring deployed on 30 critical assets; weekly alert reviews tied to CMMS work orders.
The Result: $36K/year saved through extended component life, plus $20K/year in avoided downtime. ROI = 3X.

Producer of Snacks

The Challenge: OEE at 79%; frequent failures despite manual vibration inspections.
The Approach: Implemented CBM with structured alert triage and protected execution windows.
The Result: $412,518 saved in 12 months, improved alert-to-action rates, and fewer false positives after model learning.

The Power of Process + Technology

The takeaway from these examples is clear:
CBM works when it’s embedded in a work management process that ensures every credible alert becomes a completed job before failure.

Without that process, even the most advanced predictive models won’t prevent breakdowns — because the real bottleneck isn’t detection, it’s execution.

Key Lessons from Industry Research

When we reviewed ten of the most respected sources on Condition-Based Maintenance and planning & scheduling — including Reliabilityweb, IDCON, the U.S. DoD CBM+ Guidebook, and Maintworld — the conclusions were remarkably consistent.

Here are the five big lessons you can take to your own plant:

1. Technology Is Not the Bottleneck — Process Is

Modern CBM platforms are accurate and reliable. They can detect failure conditions with plenty of lead time. The real challenge is converting that insight into action before the P-F interval expires.

If your team is trapped in a reactive cycle — firefighting breakdowns, postponing proactive work — the best technology in the world won’t save you. The process, culture, and discipline to act on early warnings matter more than the sophistication of the sensors.

2. Prioritisation Is Essential

One of the biggest mistakes plants make is treating all alerts as equal. Without a clear severity scale and agreed response rules, teams either:

Overreact — pulling resources for non-critical issues, causing unnecessary disruption.
Underreact — letting critical issues slip until they become breakdowns.

By mapping alert severity to specific timeframes for planning and scheduling, you ensure that resources are allocated where they deliver the most value.

3. Protect the Weekly Schedule

The frozen weekly schedule is one of the most powerful tools for increasing planned work and reducing reactive work. Without it, today’s emergencies — and sometimes just “noisy” requests — will continually bump proactive jobs, including CBM interventions.

When CBM work is on the frozen schedule, it gets done. When it’s not, it’s vulnerable to being postponed until the point of failure.

4. Choose Assets Strategically

Not every asset is a good candidate for CBM. Start where you can:

Demonstrate value quickly (high downtime cost, clear failure modes).
Build team confidence in the system.
Refine your alert-to-action process on a manageable scale.

Once you’ve proven the value — and worked out the wrinkles in your process — then scale to more assets and more complex monitoring.

5. Communicate Success in Business Language

A reliability engineer might be excited about reducing vibration from 5.2 mm/s to 2.1 mm/s. Your CFO probably won’t be.

To win and keep leadership support, translate technical wins into business outcomes:

Hours of downtime avoided.
Dollar value of production loss prevented.
Maintenance costs saved.
ROI percentages.

AI predictive maintenance use cases in food manufacturing hero

An Action Plan for CBM Success

You don’t need a complete overhaul to make CBM deliver ROI. Start small, follow a structured plan, and build momentum.

Here’s a practical starting point you can apply in any industrial setting:

Phase 1 — Stabilise Work Management

Appoint a dedicated planner.
Create and protect a frozen weekly schedule.
Track schedule compliance every week.
Hold weekly planning meetings with Operations and Maintenance.

Goal: Create capacity for proactive work.

Phase 2 — Select Pilot Assets

Choose 20–50 assets that meet your criteria: high downtime cost, clear failure modes, measurable condition indicators.
Ensure they have a P-F interval long enough to act.

Goal: Set yourself up for early wins.

Phase 3 — Establish Triage Rules

Define who reviews alerts and how quickly.
Agree on severity levels and the associated action timelines.
Document this process so it’s consistent across shifts.

Goal: Ensure alerts are acted on with urgency proportional to their impact.

Phase 4 — Integrate with CMMS

Auto-create work orders from CBM alerts with all relevant details.
Link alerts to historical asset data.
Require feedback on completion.

Goal: Close the loop from detection to execution.

Phase 5 — Measure and Communicate ROI

Track avoided downtime and dollar savings.
Measure planned work %, schedule compliance, and alert closure rates.
Report in terms the business understands.

Goal: Build executive confidence to expand the program.

The ROI Opportunity: Why This Matters Now

In agri-food manufacturing, downtime can cost anywhere from a few thousand to tens of thousands of dollars per hour. And unlike some industries, you can’t just “make it up later” — lost production often means lost revenue forever.

When CBM is paired with robust planning and scheduling:

Failures are prevented before they cause disruption.
Maintenance costs drop as you replace components based on actual condition, not arbitrary schedules.
OEE improves as unplanned downtime is reduced.
Maintenance team morale improves as they move from firefighting to proactive work.

And because these results are measurable, they build a business case for continued investment in predictive capabilities.

Final Thoughts: Your CBM Program Can Succeed

Condition-Based Maintenance isn’t a silver bullet — but it is an essential part of a modern reliability strategy. The difference between a CBM program that fails and one that delivers six-figure savings is not the quality of the sensors or the AI.

It’s whether your organisation has:

The discipline to plan and schedule CBM work.
The capacity to act within the P-F window.
The clarity to prioritise alerts and execute them before failure.
The communication to show leadership the business value.

Without these, CBM is just a cost line on your budget. With them, it’s a revenue protector.

Your Next Step: Turn Alerts into Action with Factory AI

At Factory AI, we don’t just provide accurate early-warning alerts. We work with your team to ensure those alerts become completed jobs — before failure — through structured integration with your planning and scheduling process.

That’s why our pilots are designed to prove ROI within 90 days, with hardware costs redeemable against your subscription. You’ll see avoided downtime in hours and dollars, not just vibration graphs.

If you’re ready to move from CBM-as-data to CBM-as-savings, we’d love to show you how.

Book your pilot today and see how much value you can unlock when predictive insight meets disciplined execution.