Cmms
How does CMMS support Reliability-Centered Maintenance (RCM)?
CMMS captures structured failure data per asset, tracks MTBF/MTTR by asset class, supports RCM analysis workflows, and links maintenance history to reliability program inputs to define optimal maintenance strategies.
What is the critical difference between CMMS and EAM (Enterprise Asset Management)?
CMMS is specialized for maintenance operations (work orders, PM schedules, spare parts); EAM encompasses CMMS plus broader enterprise functions like financial/budgeting, materials management, and full lifecycle reliability functionality.
Which KPIs must a CMMS reliably track for asset reliability?
Essential metrics include Mean Time Between Failures (MTBF), Mean Time To Repair (MTTR), PM compliance, schedule compliance, and wrench time, all derived from clean failure coding and work-order history.
A Computerized Maintenance Management System (CMMS) is specialized software that centralizes maintenance information to automate, schedule, track, and report on all maintenance activities, assets, work orders, spare parts, and failure history, serving as the single source of truth for maintenance operations.
In reliability engineering, CMMS is the operational backbone that enables structured failure coding, maintains a criticality-classified asset register, manages PM libraries tuned to specific failure modes, tracks KPIs like MTBF and MTTR, and integrates with condition-monitoring tools to shift from reactive firefighting to proactive, data-driven maintenance.
Treating CMMS as a digital logbook only for issuing/closing work orders without capturing structured failure data for analysis, rendering reliability engineering a disconnected spreadsheet exercise.
Inconsistent or missing failure coding, which prevents the system from identifying repeat failures, calculating accurate reliability metrics, or informing PM optimization.
Lack of integration between CMMS and condition-monitoring/predictive tools, creating data silos that block the feedback loop needed to turn repair data into predictive insights.