Asset Hierarchy
Any depth: Plant → Line → Cell → Machine → Sub-unit. Breakdowns, work orders and KPIs roll up correctly at every level — the line manager sees the line, the plant head sees the plant.
Module 07 · Maintenance
A CMMS designed for manufacturing — not retrofitted to it. Asset hierarchy at any depth, work orders for every category from corrective to turnaround, predictive analytics on the same IoT data feeding SPC and OEE, and MTBF/MTTR tracking at every node.
What it does
Six capabilities, each tied to a decision your team is already trying to make.
Any depth: Plant → Line → Cell → Machine → Sub-unit. Breakdowns, work orders and KPIs roll up correctly at every level — the line manager sees the line, the plant head sees the plant.
Corrective, Preventive, Predictive, Changeover and Turnaround work order types, each with its own workflow, approvals and skill-routing.
Time-based, usage- or cycle-count-based, and condition-based triggers — with lead-time alerts so spares are staged and technicians are scheduled before the PM is due.
Ingest IoT sensor data via Zometric Edge and let the AI layer flag the failure mode before the breakdown. The shift from reactive to predictive maintenance becomes a deployment, not a project.
Centralised spare register, reorder alerts, consumption analytics and cost-per-asset tracking. Production scheduling is locked against assets that are under maintenance.
MTBF, MTTR, OEE contribution, PM compliance, cost-per-asset. Auto-generated shift and monthly MIS reports — the maintenance manager stops writing the report and starts reading it.
Standards & Compliance
Better Together
Zometric's modules share a single data backbone — adopting more of them compounds the signal-to-noise advantage.
Connect any machine, PLC, sensor or database to the platform.
Automated production logs, real-time OEE, downtime tracking, job scheduling.
From idea to financial impact — structured CI pipeline.
The analytical brain — comprehensive statistics engine with AI-generated plain-language interpretations on every output.
A 30-minute discovery call. A 2–3 week pilot on real factory data. Then scale at your pace.