Every constraint your plant has.
One feasible answer in 30 seconds.
Machine eligibility, shifts, maintenance, setup matrices, materials, due dates - solved together, not in spreadsheets.
Hard constraints.
All of them. Every cycle.
Skip one and the schedule is wishful thinking. KFactory honours all eight, simultaneously.
Machine eligibility
Which operations can run where, with required tooling and skill.
Shift patterns
Up to 3 shifts per day, including weekends and holidays.
Maintenance windows
Planned downtime treated as a hard block, not an afterthought.
Setup matrix
Sequence-dependent changeovers - A→B costs more than A→A.
Batch splitting
Split a batch across machines when speed beats setup cost.
Transfer times
Move time between machines, including conveyor and AGV legs.
Parallel lanes
Multi-lane processing on machines that run >1 job at a time.
Due dates
Customer commitments - soft, hard, or contractually penalised.
One solver. Three answers.
Pick the objective. The solver finds the best feasible schedule for it.
Minimise makespan
Finish the whole batch as early as possible - even if some orders idle.
Minimise lateness
Hit every due date - accept some idle time as the price of on-time delivery.
Maximise utilisation
Keep every machine busy. Trade some lateness for higher throughput.
Where the solver meets the human.
Drag-and-drop Gantt
Move a task, pin it, and replan the rest - the solver respects your decision.
Replan rest of week...
Natural-language assistant
Ask in plain English. Powered by Microsoft Semantic Kernel.
KF: Done. 3 changes, 0 slips.
What-if scenarios
Test a rush order before committing. Compare KPIs side-by-side.
+1.2h makespan · 0 slips · save?
The week before
the week breaks.
See what KFactory Plan
could be worth to you.
Adjust the sliders to match your operation. The annual impact updates live, based on APICS/ASCM advanced-planning-and-scheduling benchmarks.
How it's calculated: Changeover savings (orders × changeover hours × hourly cost × 52 weeks × 20% reduction) + planner-time savings (hours × hourly cost × 52 weeks × 80% reduction). Based on APICS/ASCM scheduling-optimisation benchmarks.
What optimised planning
unlocks across the operation.
Four use cases the Plan engine is built for. Each one links to a deeper write-up with the methodology, integrations and KPIs.
Produce more with same resources or less
Unlock additional capacity from existing machines, people and lines. No capital investment, no new hires - the solver finds the slack your current schedule hides.
Explore use casePredict failures, optimise timing
Reduce maintenance costs by a quarter and extend equipment lifespan by 15-20%. Schedule maintenance around the production plan instead of against it.
Explore use caseOptimise production scheduling
Improve throughput across the same footprint, and free planners from the hours of manual scheduling work that delivered worse results anyway.
Explore use caseOptimise purchasing, reduce inventory costs
Cut inventory carrying costs by 20-30% while preventing stockouts. Built-in MRP turns the schedule into an exact buying list, with safety stock and lead-time bucketing handled.
Explore use caseBuilt to be trusted.
| Specification | Detail |
|---|---|
| Solver Engine | Google OR-Tools CP-SAT |
| Schedule Generation | <30 seconds for typical scenarios |
| Constraint Types | Machine eligibility, shift patterns, maintenance, setup matrices, batch splitting, transfer times, parallel lanes |
| Optimisation Objectives | Minimise makespan · Minimise lateness · Maximise utilisation |
| Scheduling Modes | Forward (ASAP) · Backward (JIT) |
| AI Assistant | Microsoft Semantic Kernel - natural language scheduling |
| MRP | BOM explosion, net requirements, safety stock, shortage alerts |
| Capacity Analysis | 7 / 14 / 30-day bottleneck detection |
| Export | PDF · Excel · CSV |
| ERP Integration | Standard API - orders, machines, materials |
Hand us
your hardest week.
We'll model your real constraints and run the solver live on a 20-minute demo.
Request a Demo