kint takt
The optimal beat of your factory. Realistic, optimal production plans with finite capacity that the shop floor does not override.
The planning gap
MRP plans a factory that does not exist.
MRP assumes infinite capacity, so the plan is not realistic. Your planners rebuild it in Excel and override it every shift. Setup times, bottleneck machines, due dates against real capacity. None of it fits.
kint takt builds the plan that is provably feasible and provably optimal. It respects finite capacity, sequence-dependent setups, and due dates, then proves the result is the best schedule available. Your planners stop overriding it.
Finite capacity scheduling
One schedule. Provably feasible. Provably optimal.
A live plan for a six-resource furniture line. Setups minimized, the bottleneck respected, due dates met.
Paint is the binding constraint. Every extra minute there caps total throughput.
| Product | Margin € | Paint h | Assy h | Build qty |
|---|---|---|---|---|
| Oak chair | 62.00 | 0.40 | 0.55 | 28.6 |
| Walnut table | 148.00 | 0.90 | 1.20 | 11.4 |
| Pine shelf | 41.00 | 0.30 | 0.35 | 0.0 |
| Beech stool | 33.00 | 0.20 | 0.25 | 19.0 |
| Birch cabinet | 96.00 | 0.70 | 0.95 | 0.0 |
# operator describes the goal in one line
"Maximize weekly contribution margin across
our 5 furniture products. We have 6 resources
with finite hours. Paint and assembly are
tight. Respect each product's resource use."# generated, fully transparent maximize Σ margin[p] · x[p] s.t. Σ use[p,r] · x[p] ≤ cap[r] ∀ r ∈ resources x[p] ≥ 0 ∀ p ∈ products # solver: HiGHS status: OPTIMAL obj: 4821.43
Energy-heavy operations moved into off-peak hours. Bridge to kint merit.
One engine, four modules
From the bottleneck to the energy bill.
Each module answers a scheduling decision your MRP leaves open. Run one, or run them on the same optimization brain.
Right job, right machine, right order
Machine assignment, job sequence, and setup minimization across job-shop and flow-shop lines. The plan respects sequence-dependent changeovers and proves the order is optimal.
Find the bottleneck, level the line
Finite capacity planning that balances load across stations and lines. Find the binding bottleneck, level takt time, and see exactly where capacity caps throughput.
Downtime where it costs least
Schedule predictive maintenance windows at the points that cost the least throughput. Plan downtime around due dates instead of fighting it.
Run when power is cheap
Plan energy-heavy operations around hourly energy prices without breaking due dates or capacity limits.
MRP plans a factory that does not exist. kint plans the one you actually run, proves it is feasible, and proves it is optimal.
How kint decides
The recommendation, and the reason behind it.
Every plan ships with its binding constraint, its delta against the old plan, and the savings your planners would have missed.
Optimal schedule & production mix · Shift A
Why plant managers choose takt
Three things the old plan never gave you.
Finite capacity, no overrides
Plans that respect real machine capacity and setup times. Your planners stop rebuilding them in Excel because the plan already fits the floor.
Provably optimal, fully transparent
Not a heuristic guess. A white-box model with a proven optimality gap. You see the constraints, the objective, and why each decision was made.
Setups down, adherence up
Sequence-dependent changeovers minimized, due-date adherence up, and a schedule that is provably feasible the moment it ships.
Where takt is different
Incumbents need months and a modeler. takt needs days.
The legacy APS engines are powerful and proven. They are also slow to configure, rule-based or heuristic, and heavy to roll out. takt generates the model from your description plus your data, then guarantees the plan with math.
| Their approach | kint takt | |
|---|---|---|
Rule-based APS engines legacy schedulers | Rule-based engine. 6 to 18 months to introduce. GenAI only as a helper. | Provably optimal, not rule-based. Live in days, AI builds the model. |
Enterprise constraint solvers heavy implementation | Strong constraint-based optimization, but enterprise-tier and heavy implementation. | Same optimization rigor. Cloud and API native, lighter to deploy. |
High-speed heuristic schedulers fast but heuristic | 100,000+ operations in seconds, but heuristic. No optimality guarantee. | Provably optimal with a stated gap. The math guarantees the plan. |
The other tools in the field all need a modeler or consultant to configure constraints over months. takt is more modern, more AI, and faster to go live.
Powered by the kint engine
Proof, not promises.
Made in Germany and named takt, it fits Automotive and Lean culture out of the box.
Find the optimal beat of your factory.
Send us your routings, capacities, and setup matrix. We build a provably optimal schedule on your real data in days.