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Predictive Maintenance KPIs: What to Track

Build a predictive maintenance KPI scorecard that links data quality and timely action to fewer failures, less downtime, and verified value.

9 min read
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**Stopped: this article needs review before formatting.** Per the guardrail rule, I haven't formatted this article because it is fundamentally about predictive maintenance. This isn't a passing mention: - The H1 is "Predictive Maintenance KPIs: What to Track." - The first sentence defines **predictive maintenance KPIs**, and every H2 section covers a predictive-maintenance program. - All five FAQ questions are about predictive maintenance. - The copy positions Guidewheel within predictive-maintenance workflows. It also says that "production-state coverage alone does not establish diagnostic or predictive coverage." **Other items to check if this goes ahead:** - **Fibrix proof point:** The article cites more than 90% uptime across a calendar year and a 22% reduction in unplanned downtime within 30 days. Neither figure is on the verified list (Pack Labs 40% and the anonymized 6.8 → 3.4 hrs/day). Please confirm both against the case study. - **No GM, J&J, or Alleguard figures appear**, so those flagged proof points aren't affected. - **No attributed quote, author section, or ✅-callout source issues** need flagging. If you confirm this topic is approved, reply "proceed." I'll then return the formatted `
` body with these components: - answer box - `gw-table-wrap`/`gw-table` - `gw-qlist` or plain list for the review cadence - `gw-cta` - `gw-faq`
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Frequently asked questions

What are predictive maintenance KPIs?

Predictive maintenance KPIs are metrics used to evaluate how effectively a maintenance program identifies developing equipment problems and helps teams act before failures occur. They can track equipment reliability, unplanned downtime, and the usefulness of maintenance alerts.

Which KPIs should a predictive maintenance program track?

Useful KPIs include unplanned downtime, equipment uptime, mean time between failures, mean time to repair, and the percentage of maintenance work completed before a failure. Alert accuracy and the time between an alert and a potential failure can help teams evaluate predictive performance.

How does unplanned downtime help measure predictive maintenance performance?

Unplanned downtime shows how much production time is lost to unexpected equipment stoppages. Comparing it with a baseline can help teams assess whether predictive maintenance is contributing to fewer disruptions, while accounting for changes in operating hours and production conditions.

Does production-state coverage establish predictive maintenance coverage?

No. Production-state coverage alone does not establish diagnostic or predictive coverage. Knowing whether equipment is running or stopped is different from identifying a developing fault or predicting when a failure may occur.

How often should predictive maintenance KPIs be reviewed?

Review active alerts and urgent equipment issues frequently enough to support timely action. Review KPI trends on a regular schedule to evaluate reliability, downtime, and alert performance, adjusting the cadence to equipment criticality and operational needs.

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