Audit

Predictive Maintenance Readiness Audit (PdM readiness scorecard)

Structured, interactive audit that scores sensor coverage, data quality, CMMS integration, maintenance processes, and skills/change management to assess readiness for a practical PdM pilot and produce clear next steps based on a banded readiness score.

Interactive Tool

Predictive Maintenance Readiness Audit (PdM readiness scorecard)

This short audit helps you quickly assess whether your site is ready to run a practical predictive maintenance (PdM) pilot without wasting budget on immature use cases. Complete the five section scores (0-5) and optionally add notes. The total score (maximum 25) indicates a recommended next step.

Scoring scale (0–5)

  • 0 — No capability or far from acceptable
  • 1–2 — Significant gaps; many blockers
  • 3 — Basic capability present but unstable or inconsistent
  • 4 — Good capability with a few gaps
  • 5 — Strong, reliable capability suitable for pilots

How to use

Answer each section with the best current site-level assessment for the asset class you plan to pilot (rotating equipment, pumps, motors, compressors, etc.). Be pragmatic: a pilot typically needs a solid 16+ overall score and minimal gaps in sensor coverage and data quality.

Band guidance (total score out of 25)

  • 0–8: Not ready — Major blockers. Focus on basic sensing, data capture, and maintenance process hygiene before attempting PdM.
  • 9–15: Partially ready — Some foundations exist but remedial work needed (data gaps, CMMS linkage, clear decision rules).
  • 16–20: Mostly ready — Good candidate for a scoped pilot if you address a couple of prioritized gaps.
  • 21–25: Ready — Strong readiness. Proceed with a well-scoped pilot focused on clear business outcomes.
Are sensors installed at the right points and in sufficient quantity to capture common failure modes? Consider sampling rate, placement, and whether critical assets lack instrumentation.
List which asset types have sensors, which failure modes are uncovered, known blind spots, and tagging/asset-identification issues.
Is sensor data reliable, time-synced, labeled, and retained long enough for modeling? Consider gaps, noise, missing timestamps, and accessibility for analytics.
Mention retention policies, known gaps, missing metadata, and whether you can export data for offline analysis.
Can PdM alerts be mapped to assets and automatically create or trigger work orders? Are asset IDs, failure codes, and spares lists consistent?
Note missing links, manual handoffs, lack of auto-WO, or inconsistent asset master data.
Are there clear escalation paths and documented decision rules (when to inspect, when to act)? Are acceptance criteria and permitted downtime windows defined?
Capture existing SOPs, thresholds, risk tolerances, spares availability, and any regulatory constraints.
Do maintenance and operations teams understand PdM outputs? Is leadership committed to acting on predictions, and are training and roles defined?
Identify training needs, vendor dependence, presence of local champions, and communication across shifts.
Add the five section scores and enter the total here. This helps tracking over time. (Max 25)
Summarize the highest-impact gaps and the top 3 remedial actions you would take before or during a pilot.
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