Real Problems. Measurable Results.

Every use case follows the same pattern: identify the pain, deploy IRIS modules, and measure the impact — typically within the first 90 days.

Production Monitoring & OEE

The Challenge

Operators collect data by hand, OEE numbers arrive hours late, and decisions are always reactive — never predictive.

How IRIS Solves It

IRIS connects IoT sensors and MES data in real time, delivering live OEE dashboards with AI-driven root cause analysis so you act on facts, not hunches.

IoTMESKPI

15–25% OEE improvement within 90 days

Predictive Maintenance

The Challenge

Unplanned breakdowns halt production, reactive maintenance burns budgets, and spare parts inventory spirals out of control.

How IRIS Solves It

IRIS ingests vibration, temperature, and current data from equipment sensors, applies ML models, and simulates failure scenarios with Digital Twin — so you fix machines before they fail.

IoTCMMSDigital TwinDATA_AI

30–40% reduction in unplanned downtime

Quality Management

The Challenge

Defects are caught too late, inspections are manual and inconsistent, and tracing a quality issue back to its root cause takes days.

How IRIS Solves It

IRIS automates inspection triggers, predicts quality deviations with AI, and correlates defects to specific suppliers, batches, or process parameters in seconds.

QMSIoTDATA_AI

20–35% reduction in quality costs

Warehouse & Material Flow

The Challenge

Material shortages stall the line, warehouse layouts are chaotic, and gate scheduling is a manual guessing game.

How IRIS Solves It

IRIS WMS optimizes material flow with AI, integrates MRP for demand-driven replenishment, and supports AGV/AMR coordination for lights-out logistics.

WMSMRPIoT

25% improvement in warehouse throughput

Energy & Sustainability

The Challenge

Energy consumption is a black box, ESG reports are assembled manually each quarter, and carbon tracking has blind spots.

How IRIS Solves It

IRIS monitors energy in real time at the machine level, uses AI to optimize consumption patterns, and auto-generates ESG compliance reports.

ESGIoTKPI

10–20% energy cost reduction

AI-Powered Production Planning

The Challenge

Static schedules can't handle disruptions, what-if analysis doesn't exist, and planners optimize by gut feel.

How IRIS Solves It

IRIS APS combines Digital Twin simulation with AI scenario optimization, handling hundreds of constraints to build schedules that actually survive contact with reality.

APSDigital TwinDATA_AI

20–30% improvement in schedule adherence

Which Challenge Is Costing You the Most?

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