How to choose predictive maintenance software
Choose predictive maintenance software by starting from your critical assets and data, not the feature list: match the approach (sensor vs analytics) to those assets, check it integrates with your CMMS, insist on a clear pilot with a measurable target, and weigh total cost against failure cost.
| Topic | Key point |
|---|---|
| Start from your assets, not the features | The most common mistake is shopping by feature list. |
| What to check | Fit to your assets: sensor-based vs analytics-based, and proven on equipment like yours. |
| Questions to ask vendors | Ask plain, specific questions and expect direct answers: what exact problem does this solve, what data do you need from us, how long until we see a re |
| Insist on a pilot | Never buy on the demo. Run a time-boxed pilot on a defined set of critical assets with a measurable success target — faults caught, downtime avoided. |
Start from your assets, not the features
The most common mistake is shopping by feature list. Start instead from your critical, costly-to-fail assets and the data you already have. That tells you whether you need a sensor-based platform (best for rotating equipment) or an analytics-based one (best for covering many assets from existing data) — or both. The right tool follows from the problem, not the demo.
What to check
- Fit to your assets: sensor-based vs analytics-based, and proven on equipment like yours.
- Integration: does it connect to your CMMS/EAM and historian, so alerts become work orders?
- Acting on alerts: how easily does a detection turn into a prioritised, actionable work order — not just a dashboard?
- Data needs: what data does it require from you, and is yours good enough?
- Total cost: hardware, subscription and the people-time to run it.
Questions to ask vendors
Ask plain, specific questions and expect direct answers: what exact problem does this solve, what data do you need from us, how long until we see a result, what does success look like in numbers, and who else in our sector uses it? A good vendor is candid about what their tool cannot do. Be wary of anyone promising magic without explaining method, data needs and limits.
Insist on a pilot
Never buy on the demo. Run a time-boxed pilot on a defined set of critical assets with a measurable success target — faults caught, downtime avoided. If it hits the number, scale it; if not, you have spent little and learned a lot. A disciplined, problem-first, pilot-driven choice is how you avoid expensive shelfware.
Frequently asked questions
How do I choose predictive maintenance software?
Start from your critical assets and existing data, not the feature list. Match the approach (sensor-based for rotating equipment, analytics-based to cover many assets from existing data), check it integrates with your CMMS so alerts become work orders, weigh total cost against failure cost, and insist on a pilot with a measurable target.
What should I ask a predictive maintenance vendor?
Ask what exact problem it solves, what data it needs from you, how long until a result, what success looks like in numbers, and who else in your sector uses it. A good vendor is candid about limitations; be wary of anyone promising magic without explaining method, data needs and limits.
Should I run a pilot before buying predictive maintenance software?
Yes. Never buy on the demo. Run a time-boxed pilot on a defined set of critical assets with a measurable target — faults caught, downtime avoided. Scale it if it hits the number; if not, you have spent little and learned a lot.
Related guides
Sensor-based vs analytics-based predictive maintenance
Sensor-based predictive maintenance adds condition sensors to specific machines — fast and accurate on rotating equipment, but costs per machine. Analytics-based models existing historian and SCADA data to cover many assets without new sensors — better for scale, but dependent on data quality.
How much does predictive maintenance cost?
Predictive maintenance cost has three parts: monitoring hardware (for sensor-based approaches, priced per asset), software or analytics (often per-asset or per-site subscription), and the people-time to act on findings. Analytics on existing data scales cheaper than sensors on every machine.
CMMS vs EAM
A CMMS manages maintenance — work orders, PMs, spares. An EAM is broader, managing the whole asset lifecycle including procurement, finance and multi-site operations. Smaller maintenance teams usually need a CMMS; large asset-intensive enterprises lean to EAM.
Software that helps
Augury
Machine health monitoring for rotating equipment using vibration and AI.
Siemens Senseye Predictive Maintenance
Scalable predictive maintenance that learns from existing condition data.
AVEVA Predictive Analytics
Early-warning analytics for critical process and power assets.