The downtime problem PdM exists to solve
global predictive-maintenance market from 2026 to 2031 (~34% CAGR) — a signal of adoption momentum, not of guaranteed returns (Mordor Intelligence).
Source: Mordor Intelligence
lost annually to unplanned downtime by the world’s 500 largest companies — about 11% of total revenue — the problem PdM exists to reduce (Siemens, via AEMT).
Source: Siemens / AEMT
increase in equipment uptime and availability that predictive maintenance can deliver — actual gains depend heavily on data quality (Deloitte).
Source: Deloitte
reduction in overall maintenance costs typically attributed to PdM, alongside 20–50% less time spent planning maintenance (Deloitte).
Source: Deloitte
How predictive maintenance works — the key terms
- Predictive maintenance (PdM)
- A maintenance strategy that uses live equipment data and models to predict when a specific asset will fail, so service happens just before failure — unlike reactive maintenance (fix after breakdown) or preventive maintenance (fix on a fixed schedule regardless of condition).
- Condition monitoring
- Continuously measuring physical signals from equipment — vibration, temperature, acoustic emission, oil particulates, current, pressure — to assess health in real time. It is the sensing layer that feeds a PdM model; condition monitoring tells you the state now, PdM predicts the state later.
- Anomaly detection
- A modeling technique that learns an asset’s normal operating signature and flags deviations from it. It is useful in the cold-start phase because it needs mostly healthy-running data rather than labeled failures, though it detects that something is wrong, not necessarily what or when it will fail.
- Remaining useful life (RUL)
- A model’s estimate of how much operating time or how many cycles an asset has left before failure. RUL predictions require historical run-to-failure data to train against, and are what turn an alert into a scheduling decision — “this pump has roughly three weeks,” not just “this pump looks abnormal.”
- CMMS / EAM
- A Computerized Maintenance Management System (or broader Enterprise Asset Management platform) is the system of record for assets, work orders, and maintenance history. PdM delivers value only when its alerts flow into the CMMS as actionable work orders — an insight nobody acts on is worthless.
How to evaluate a PdM builder — a checklist
Data readiness first, explainable models, and alerts that become work orders.
- Start with a data-readiness assessment: does the vendor audit your sensor coverage, data historian, and — critically — your failure history before quoting a solution or an outcome?
- Confirm you have (or can capture) labeled failure events. Without run-to-failure history, expect an anomaly-detection starting point and be honest about the cold-start ramp rather than expecting RUL predictions on day one.
- Insist on explainability. You should be able to see why the model flagged an asset (which signals, which thresholds), not just a black-box red light your technicians won’t trust.
- Require integration into your CMMS/EAM so predictions become work orders in the tools your team already uses — not a separate dashboard nobody checks.
- Ask how accuracy is measured and reported over time (precision/recall, false-alarm rate, lead time before failure), and how the model is retrained as conditions change.
- Pin down data ownership, security, and where models and telemetry live — especially for OT networks and any cloud connectivity.
- Scope a bounded pilot on a few critical assets with a clear success metric before any plant-wide rollout, and agree on what “success” means in writing.
- Evaluate build-vs-buy honestly: an off-the-shelf platform is faster for standard rotating equipment, while a custom build fits unusual assets, proprietary data, or tight system integration — weigh both against your in-house support capacity.
Frequently asked questions
How is predictive maintenance different from preventive maintenance?
Preventive maintenance services assets on a fixed schedule — every 500 run-hours, every quarter — whether or not the asset needs it, which wastes labor on healthy machines and still misses failures that happen off-schedule. Predictive maintenance watches each asset’s actual condition and predicts failure for that specific machine, so you intervene only when the data warrants it. Preventive is calendar-driven; predictive is condition- and forecast-driven.
What data do we need before we can start?
At minimum, sensor or telemetry data that reflects asset health (vibration, temperature, current, pressure, and similar) plus historical maintenance and failure records. The single biggest determinant of results is failure history: models that predict remaining useful life need examples of past failures to learn from. If you lack labeled failures, you can begin with anomaly detection and accumulate labeled events over time — this is the “cold-start” reality, and a credible builder will name it up front.
What ROI is realistic?
Authoritative estimates put PdM’s benefit at roughly a 10–20% increase in equipment uptime and a 5–10% reduction in overall maintenance costs (Deloitte), with the value driven by avoiding unplanned downtime that costs the world’s largest firms about 11% of revenue (Siemens). But these are ranges, not guarantees: your result depends on data quality, which assets you target, and whether alerts actually turn into completed work orders. Model the payback on your own downtime cost per hour, and treat any promised fixed percentage skeptically.
What are the red flags when evaluating a PdM vendor?
Three big ones: a guaranteed downtime-reduction number quoted before anyone has seen your data; no data-readiness assessment (they’ll deploy the same model everywhere regardless of your sensor coverage or failure history); and a black-box model with no explainability, so your technicians can’t see why an asset was flagged and won’t trust or act on it. Also watch for solutions that don’t integrate with your CMMS — an alert nobody works is worthless.
Should we buy an off-the-shelf platform or have something built?
Off-the-shelf platforms are faster and cost-effective for common rotating equipment (motors, pumps, gearboxes) with well-understood failure modes. A custom build makes sense when you have unusual assets, proprietary process data, strict integration needs, or want the models and data to remain fully yours. Many operators do both: a platform for standard assets, custom work for the equipment that actually drives their downtime risk. Weigh each against the in-house capacity you’ll need to sustain it.
Where does a custom-AI firm like CONE RED fit?
CONE RED builds custom AI — analytics, agents, and LLM/RAG systems — for industrial, logistics, financial-services, and healthcare operators, which is the profile of a bespoke PdM build rather than a shrink-wrapped platform. It works on a feasibility phase of about six weeks and production in roughly 90 days, and it measures model accuracy but does not guarantee it — the honest posture to expect from any serious builder. It is offered here as one option to evaluate alongside off-the-shelf platforms and other vendors, not the only choice.
PdM ROI ranges are industry estimates, not guarantees — actual results depend on your data quality and whether alerts become completed work orders. CONE RED’s ~6-week feasibility / ~90-day production timeline is a first-party positioning claim; model accuracy is measured per engagement, not guaranteed.