A heat schedule in an EAF melt shop is a puzzle with many pieces. Furnace slots, ladle availability, the ladle furnace, the caster sequence, scrap readiness and maintenance windows all have to line up, and the plan changes whenever one of them slips. Most shops solve it with experience and a spreadsheet, which works until demand charges and delays enter the picture. AI scheduling treats them all as constraints to solve together, and melt shops can ask iFactory AI how heat scheduling is solved across furnaces and downstream units.
Let AI Solve the Heat Schedule While You Run the Melt Shop
iFactory AI allocates furnace slots, respects downstream constraints and shapes power demand in one plan, so heats flow without avoidable waits or peaks.
What a Good Heat Schedule Must Respect
A schedule is only as good as the constraints it honours. These six cover most melt shops.
Furnace availability
Slots depend on refractory, electrode and maintenance status.
Ladle and ladle furnace
Ladles and treatment stations limit how fast heats can move on.
Caster sequence
Continuous casting wants an unbroken sequence of compatible heats.
Charge readiness
Scrap, DRI or hot metal must be ready for each charge.
Grade and chemistry
Compatible grades must follow each other to protect the sequence.
Power limits
Demand targets and tariff periods shape when heats can run.
The Chain a Heat Must Travel
A heat does not end at tap. It moves through a chain, and a delay anywhere pushes back everything behind it.
Constraints and What Happens When They Break
Each constraint has a typical failure and a cost. This table shows what a good planner watches.
| Constraint | Typical Failure | Consequence |
|---|---|---|
| Ladle availability | Ladle not ready at tap | Heat waits and power-off time grows |
| Caster sequence | Incompatible grade breaks the sequence | Sequence lost and caster restart cost |
| Charge readiness | Scrap or DRI late | Furnace idle and slot wasted |
| Maintenance window | Overrun on a planned stop | Whole schedule shifts |
| Power limit | Two heats peak together | Higher demand charge for the month |
How Scheduling Maturity Progresses
Melt shops move through clear stages. Knowing yours shows the next realistic step.
See How Much Slack Your Heat Schedule Hides
Book a 30-minute session and iFactory AI will review your heat history and constraints to show where waits, overlaps and peaks can be planned out.
Manual Planning or AI Solving
Experienced planners are very good, but they hold a limited number of constraints in mind at once. The comparison is not about skill, but about scale.
What Usually Disrupts a Schedule
Plans break for familiar reasons. The chart shows an illustrative ranking of common disruptions.
Daily Scheduling Review Checklist
Use this list at the start of every shift. Each box should be answerable from the planning screen.
A Composite Scenario: The Slot That Kept Slipping
This is a composite example, not a named plant. It shows how solving constraints together reduces waits.
Getting Started in Four Steps
A first project can run alongside your current planning without disruption.
Record heat times, delays and constraints from recent weeks.
Build the constraint model with the planners.
Run AI plans beside the manual plan and compare.
Switch on live plans once the team trusts them.
What iFactory AI Adds to Heat Scheduling
iFactory AI is smart manufacturing and industrial software for steel and other heavy industry. Explore heat scheduling in a live product tour on realistic melt shop data.
Review of AI Heat Scheduling
A practical review of what makes AI scheduling useful in a melt shop, written as editorial guidance rather than a named expert quote.
Model the whole chain
A schedule that stops at tap will fail at the ladle or caster.
Run in shadow first
Comparing AI plans with manual plans builds trust without risk.
Keep planners in charge
Planners should be able to override, because the system learns from their reasons.
Frequently Asked Questions
What does AI heat scheduling actually do?
It allocates furnace slots and sequences heats while respecting ladle, caster, charge and power constraints together. See an AI heat schedule built on realistic data in a short demo. It replaces guesswork with a plan that can be re-run in seconds. The plan is only as good as its inputs, so ladle turnaround, treatment times and caster sequence rules should be entered as accurately as possible.
Can it replace our planners?
No. It handles the heavy constraint solving, while planners decide priorities and handle exceptions. Their overrides also teach the system. The best results come from planners and AI working together. Planners also stay in charge of priorities such as urgent orders or special grades, which the software then plans around.
How does it help with demand charges?
It spaces high-power phases of different furnaces across the interval, so peaks do not coincide. Ask support how demand shaping is built into the plan for your tariff. This often saves more than late-stage limits. Because the same plan also reflects tariff periods, it can lower energy cost and demand charges without a separate exercise.
What data does it need?
Heat times, delays, ladle and caster records, and the constraints your planners already use. Much of this exists in logs and spreadsheets. Gaps can be filled step by step. Even partial data works for a first shadow run, and the plan improves as more delays and constraints are recorded.
How long before we see value?
Many shops see useful plans in shadow mode within weeks. Book a call to scope a shadow-mode scheduling pilot with your planning team. A clear before and after builds the case for going live. Running the AI plan beside the manual plan for a few weeks gives a fair, low-risk comparison before anyone relies on it.
Plan Every Heat With the Whole Melt Shop in View
iFactory AI solves furnace slots, downstream constraints and demand shaping in one plan. Book a walkthrough to see it on your own heat history.
