When downtime hits, the cost is immediate

A single line stop can ripple across an entire plant. Missed orders. Overtime. Expedited shipping costs. Customer dissatisfaction.

Yet most factories still operate reactively:

• Maintenance schedules follow manufacturer recommendations, not real-time usage

• Sensor alerts may be siloed or ignored

• Production managers scramble when machines fail unexpectedly

Downtime isn’t just inconvenient — it’s expensive, operationally disruptive, and avoidable.

Why predicting failures is hard

Predictive maintenance requires correlating complex, high-volume datasets:

• Telemetry streams from machines and sensors

• Historical maintenance logs

• Production schedules and planned downtime windows

• Operational KPIs such as throughput, cycle time, and quality metrics

Without automated workflows, detecting patterns early enough to prevent failure is nearly impossible. Teams either over-maintain (wasting resources) or under-maintain (risking downtime).

How Mage makes it work

Mage orchestrates manufacturing data into reliable, continuous workflows that surface predictive insights before failures occur:

• Ingest machine telemetry, logs, and production schedules

• Normalize sensor readings and historical maintenance events

• Identify patterns and early warning signals for potential equipment issues

• Trigger alerts, workflow actions, or schedule adjustments automatically

Maintenance teams gain a proactive view, and production planners can adjust schedules to minimize disruption.

What this looks like in practice

• Operators receive early notifications of potential issues on critical equipment

• Maintenance teams schedule interventions during low-impact periods

• Production managers shift or stagger workloads to avoid line stoppages

• Plant leadership monitors real-time dashboards for uptime and efficiency

Instead of reacting to every breakdown, factories operate predictively, keeping lines running and costs controlled.

Outcomes for teams

• Reduced unplanned downtime and production delays

• Optimized maintenance schedules with minimal disruption

• Improved operational efficiency and throughput

• Increased confidence in plant performance