Predictive Maintenance Readiness Audit

Use this structured audit to discover which assets, data, and processes are ready for predictive maintenance pilots—and where to focus effort so pilots succeed without wasting budget.

What you'll understand and accomplish

This audit guides you through practical evidence-based checks so you can: assess data quality and historical failure records, map sensor and condition-monitoring gaps, clarify dominant failure modes, verify maintenance workflows and execution, evaluate team skills and governance, and define a realistic pilot scope with measurable success criteria.

Who this helps

Maintenance managers, reliability engineers, plant managers, operations leaders, and continuous-improvement teams at manufacturers of every size will benefit. Examples: a small job shop deciding whether to sensor a CNC lathe, a food-packaging line prioritizing motors and conveyors, a midsize metal fabricator deciding which presses to pilot, or a hospital engineering team assessing imaging equipment. This audit is for teams planning a pilot—not for those expecting an immediate turnkey predictive model or guaranteed ROI.

How the audit protects your budget and timelines

Instead of jumping straight to vendors or proofs-of-concept, the audit helps you avoid common failure modes: projects launched with poor or missing data, unclear failure definitions, inadequate historical records, insufficient sensor coverage, immature maintenance processes, or unrealistic vendor promises. You’ll get clear red flags and practical mitigation actions so pilots are scoped to win.

Practical outputs and next steps

Typical audit deliverables you can expect to produce or use: an asset readiness scorecard, a prioritized list of candidate assets, a minimum data checklist (sampling rates, telemetry, failure labels), recommended sensor and integration options, proposed pilot success metrics and duration, quick-win opportunities, and governance/skill-gap recommendations. Use these outputs to scope a single focused pilot, plan sensor rollouts, improve data capture, or build a business case informed by realistic assumptions.