advisory statement
The client faced frequent unplanned equipment failures and production interruptions due to reactive maintenance practices. Operational data existed but was fragmented across multiple systems.
Our Approach
Corrplus deployed an integrated IoT sensor network and predictive maintenance AI layer. Real-time data was streamed to a unified analytics dashboard, identifying early warning signals for mechanical stress, power anomalies, and process inefficiencies.
Results & Outcomes
Downtime reduced by 27% across 12 facilities
Maintenance costs reduced by 18%
Predictive alerts issued 3 days before critical failure events
Centralized visibility across all operational sites
project milestones
Sensor Deployment
Installed IoT endpoints across all production lines to collect live operational data.
Predictive AI Engine
Developed algorithms to detect mechanical stress and trigger proactive maintenance.
Control Dashboard
Designed a unified monitoring interface for all factory operations in real time.
Operational Scale-Up
Extended automation to 12 facilities with continuous ROI tracking and reporting.
brief description
Corrplus helped InduMach Systems to digitise and optimise 12 production facilities using AI-driven IoT automation. This reduced downtime by 27% and improved operational insight.
client
InduMach Systems
project budget
$3.4M
date started
4 March 2024
