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Predictive Maintenance Guide for Phinisi Fleets
Predictive maintenance starts with consistent structure. Most fleets fail here because failure logs, work orders, and parts records use different naming and no shared ownership model.
1. Standardize one failure taxonomy
Use one list for subsystem, failure mode, and root cause across all vessels. This is the only way to compare reliability trends without manual interpretation each month.
2. Pair engine-hour scheduling with downtime events
Calendar-only plans miss high-utilization vessels. Schedule by engine-hour and map every breakdown to downtime impact so preventive priorities match commercial risk.
3. Build route-specific critical parts matrix
Define min/max levels per route group and vessel class. A part that is low-risk near base port can become high-risk on remote itineraries with long lead times.
| Matrix field | Example use |
|---|---|
| part_criticality | Blocks dispatch if unavailable |
| lead_time_days | Sets reorder trigger window |
| route_group | Applies higher buffer for remote routes |
| alternate_part_ref | Fallback plan during shortage |
4. Run a weekly reliability close
Your weekly close should include overdue preventive tasks, open critical incidents, MTBF/MTTR trend, and next-week readiness risk. Keep one owner per exception.
90-day target profile
- Preventive compliance above 90% in pilot scope.
- Root-cause captured on all unplanned failures.
- Critical stockout events trending toward zero.
- Dispatch reliability improves across pilot routes.