Client

Hindalco Alumina Division

Industry

Metals & Mining

Implementation

14 Weeks

Algorithmic Scheduling Unlocks 22% Higher Kiln Utilization

Inquizity implemented a hybrid AI-optimization system to automate Hindalco’s multi-grade kiln scheduling, overcoming sequence dependencies and energy inefficiencies that manual Excel planning couldn’t resolve.

Project Specifics

  • Grade Complexity
  • Energy Optimization
  • Changeover Intelligence
  • Operator Adoption

Scope

  • 4 rotary kilns

  • 120+ specialty alumina grades

  • 9-month historical data

Technical Stack

  • MILP core with heuristic adapters

  • SAP PP-PI integration

  • Digital twin for thermal modeling

Timeline

  • Design: 3 weeks
  • Pilot: 6 weeks
  • Scale: 5 weeks

The Challenge

Hindalco’s specialty alumina production faced critical bottlenecks:

  • Sequence Chaos: 120+ product grades with complex changeover rules (e.g., 8hr refractory cooling between certain batches)
  • Excel Gridlock: Monthly scheduling took 3 weeks across 4 planners, with 14+ spreadsheet versions
  • Hidden Costs: Suboptimal kiln sequencing led to $1.8M/year in preventable energy waste"*

Our Approach

We co-designed a solution that respects both chemistry and operations

01

Digital Twin

Mirroring kiln thermodynamics and material properties

02

Hybrid Scheduler

MILP for optimal sequences + heuristics for operator preferences

03

Override Dashboard

Let planners adjust algorithms with tracked rationale

Higher Utilization
0 %
Energy Savings
0 %
ROI
0 x

Results

Metric
Before
After

Kiln Utilization

68%

83%

Scheduling Time

21 Days

4 Days

Energy Cost/Ton

$18.70

$15.90

The model sequences batches we’d never consider and proves why they work.

Plant Manager, Hindalco