Why predictive maintenance matters in fulfilment
In high-throughput fulfilment, downtime is not a maintenance issue alone. It becomes an SLA issue, a customer trust issue, and a margin issue. Predictive maintenance is most valuable when it protects throughput during peak demand and reduces the operational volatility caused by breakdowns.
Assets that drive the most downtime risk
- conveyors and sorters
- scanners and dimensioners
- packaging machines and labelers
- robotics where present (with strict safety constraints)
- control systems and critical sensors that cause line stoppages
Data sources and what is often missing
Automation equipment generates abundant telemetry, but the missing piece is often the maintenance linkage:
- alarms and logs are not mapped to CMMS assets
- interventions are recorded as free text
- parts usage is not aligned to failure modes
- time synchronisation between systems is weak
A successful implementation requires disciplined asset mapping and event alignment.
Modelling approaches that work in warehouses
1) Alarm pattern intelligence
Repeated alarm sequences often precede failures. Modelling alarm patterns can provide early warning and prioritised inspection tasks.
2) Performance degradation modelling
Changes in throughput, motor load, vibration, or error rates can indicate impending issues.
3) Risk-based maintenance scheduling
Rather than scheduling maintenance by calendar, interventions are scheduled by risk and operational windows.
Integration into operations
Predictive maintenance must fit peak operations. That typically requires:
- risk-based work orders that are scheduled during low-volume windows
- clear severity levels to avoid interrupting flow unnecessarily
- evidence and explainability so operators trust the system
- monitoring to ensure false positives do not harm throughput
Metrics that prove impact
- reduction in unplanned line stoppages
- increased availability during peak windows
- reduced mean time to diagnose
- reduced spare part emergency procurement
- fewer SLA breaches attributable to equipment failures