FactoryOps: OEE Monitoring & Predictive Maintenance
How GetPost Labs approaches manufacturing automation. Real-time OEE, predictive maintenance, and digital quality inspections.
Executive Summary
The Problem: Manufacturers lose $800K+ annually to unplanned downtime with zero visibility into equipment health until failures occur. Quality defects are caught at end of line when rework costs are highest.
The Solution: FactoryOps provides real-time OEE monitoring, predictive maintenance from sensor data, and digital quality inspections that catch defects 3 stages earlier.
The Outcome: Breakdowns are predicted and prevented during planned windows. Quality issues are caught early when correction costs are lowest.
The Challenge
Understanding the problem space
"We were experiencing 12% unplanned downtime with no visibility into equipment health until failures occurred. A single line stoppage costs us $4,200 per hour."
— Operations Manager, Golden Valley Foods, Shepparton
Manufacturing operations depend on equipment uptime. When machines fail unexpectedly, the costs cascade: production delays, spoiled materials, overtime for catch-up, missed delivery dates, and penalty clauses.
Most manufacturers still practice reactive maintenance — fix it when it breaks. Equipment health data from sensors exists but isn't analysed. Quality inspections happen at the end of the line, catching defects at the most expensive point.
The Solution
What GetPost Labs would build
Core Capabilities
How Downtime Is Prevented
Prediction, not reaction
Equipment availability improves when maintenance moves from reactive to predictive:
Equipment Monitoring
Machine runs until it breaks. Maintenance called. 4-8 hour unplanned stoppage while parts are sourced.
Sensors detect vibration anomaly 3 days before failure. Maintenance scheduled for night shift. Zero production impact.
Quality Control
Quality checked at end of line. Defective batch discovered. 200 units need rework at highest cost point.
Digital checks at 3 stages. Defect caught at stage 1. Only 15 units affected. Correction cost 90% lower.
Performance Visibility
OEE calculated weekly from paper records. By the time report is ready, the problems have already compounded.
OEE displayed live on factory floor screens. Shift supervisor sees issues in real time. Immediate correction.
BPMN Workflow
The business process modelled
Predictive Maintenance Process
User Journey
Production Line Issue Detection
Scenario: Line 2 showing early signs of motor bearing wear. Predictive maintenance prevents breakdown.
Vibration sensor on Line 2 motor detects frequency pattern indicating early bearing wear. 3 days before typical failure
Yellow alert: "Line 2 Motor — Predictive maintenance recommended within 48 hours". SMS to Production Manager
Opens alert details. Sees sensor trend graph, predicted failure window, recommended action. Schedules maintenance for night shift
Line keeps running during day shift. Operator informed of scheduled maintenance. No unplanned stoppage
Night shift: Tech replaces bearing in planned 45-minute window. Logs work in system with parts used
Post-repair sensor readings normal. Alert cleared. Maintenance record linked to asset history
Morning review: OEE maintained at 87%. Zero unplanned downtime. $45K breakdown cost avoided
Outcome: Potential 8-hour breakdown prevented. $45K cost avoided. OEE maintained.
Interactive Prototype
Functional dashboard demonstrating the concept
OEE Dashboard
Overall Equipment Effectiveness
© 2026 GETPOST Labs. Full Stack Engineering Solutions.
Functional prototype. Click on cells and entries to see interactions.
System Context
Where FactoryOps fits in the ecosystem
Have a Similar Problem?
This is the kind of workflow automation GetPost Labs builds. If your organisation has similar challenges, we'd love to discuss how a custom solution might help.