01 · The Challenge

More production.
Same unloading assets.

A large integrated steel producer needed to support rising production without proportionally increasing unloading infrastructure.

~40% target higher daily unloading throughput while protecting downstream material availability.
Production ↑
Raw Material Flow ↑
Existing Assets
Plant
02 · Discover & Diagnose

Start with the business problem.

70+ leaders engaged in AI Discovery

The team defined the outcome, constraints, success measures, and value at stake before designing the solution.

Historical operating data then revealed that delays occurred before, during, and after unloading—and varied by asset, material, season, and operating condition.

Business Problem
Data
Delay Hotspots
Priority Decisions
03 · Build Intelligence Into Decisions

From reactive coordination to intelligent operations.

The solution brought together optimization, machine learning, Gen AI and intelligent Agents:

Operating rules, exceptions, and experienced-user knowledge were also embedded into the platform.

Intelligent
Decision Platform
Advance Visibility
Smart Sequencing
Cycle-Time Prediction
What-If Analysis
Dashboards
AI Agents
04 · The Business Value

Better decisions.
More throughput.
Existing assets.

Supported by:

Avoidable delays
Material availability
Operating visibility
Decision speed
~40%

Target increase in daily unloading throughput

05 · The CxO Takeaway

AI became useful when it stopped being about AI.

The platform was designed around the decisions managers need to make in a dynamic operating environment, not around technology for its own sake.

Where could intelligence improve one critical decision in your business?

Explore an AI Opportunity
Business objective first.
Decisions second.
Technology third.