Template

Predictive Maintenance Pilot — Success Criteria & Acceptance (Interactive Template)

An interactive, fillable template that helps teams define measurable pilot goals, data quality rules, timelines, operational acceptance thresholds, governance, rollback rules, and a scale-decision checklist for sensor-driven predictive maintenance pilots.

Interactive Tool

Predictive Maintenance Pilot — Success Criteria & Acceptance

Use this template to define clear, measurable success criteria, data requirements, timelines, governance, and scale decision rules for a sensor-driven predictive maintenance pilot. Fill required fields and use the scale decision checklist to make a fair, evidence-based scale-up decision.

Describe the reliability problem you want to solve and who is impacted (customers, line, shift, cost centers).
E.g., percent equipment downtime, mean time between failures (MTBF), number of unplanned stops per week.
Numeric target to judge success (for example: reduce downtime by 15).
Units (%, hours, stops per week).
Current measured value for the metric using the baseline period/method below.
Date range and sampling frequency used to compute baseline (e.g., last 6 months, hourly readings).
How the baseline was measured, filters/exclusions, and responsible owner for baseline data.
Minimum sensor sampling frequency required for reliable detection (enter N/A if not applicable).
Percent of expected samples that may be missing before the data is considered unusable.
If applicable, specify minimum SNR; otherwise enter N/A.
Planned start date (YYYY-MM-DD).
Total timebox for the pilot to collect sufficient examples and evaluate outcomes.
Minimum number of failure and non-failure examples or operational hours needed for validation.
Threshold beyond which alerts would create unacceptable reactive work or distraction.
Tolerance for missed failures; specify if detection must be near-perfect for safety-critical assets.
Minimum advance notice required for maintenance to plan and act (e.g., 24 hours).
Who to contact and the escalation path for operational or safety issues during the pilot.
Define clear, measurable triggers that require pausing or rolling back the pilot (e.g., sustained false positive rate > X%, data loss > Y%).
Select all checks that must be true to move from pilot to scale. Consider weighting or mandatory checks in governance.
How outcomes will be evaluated and which statistical or operational checks will be used.
List the metrics, calculation methods, measurement frequency, reporting cadence, and responsible owners.
Where sensor and model data will be stored, retention period, backup and access controls.
How operators, maintenance technicians, and supervisors will be trained and how changes will be communicated across shifts.
Include sensors, connectivity, storage, engineering hours, and maintenance labor estimates.
Document primary risks (safety, data, operational) and proposed mitigations or outstanding questions to resolve.
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