What can be optimized
- The manufacturing sequence: grouping jobs to reduce changeovers and setups.
- Assignment: which operation goes to which machine when there are alternatives.
- Shifts and overtime: covering demand at the lowest possible cost.
- Priorities: when not everything fits, deciding what goes in and what waits — based on clear criteria, not on who shouts loudest.
- Scenarios: what happens if I accept this order, add a Thursday shift, or a machine goes down.
How it works
I model your real constraints — capacity, routings, shifts, tooling, incompatibilities — and the objective we agree on: on-time delivery, overtime, number of changeovers. The model proposes; you decide. Every proposal comes with its why: which orders are at risk and what each alternative costs.
The optimization relies on proven operations-research tooling (constraint programming, solvers like OR-Tools), not on promises of magic.
Feet on the ground
- You need a measurable objective. “Doing better” can’t be optimized; “cutting overtime without delaying deliveries” can.
- You need enough data: routings, approximate times and a shift calendar. If they don’t exist, the first step is digitalization, not the model.
- The gain is measured before scaling: first a pilot on your historical data, and with that number you decide.
FAQ
Do I need “AI”?
You need decisions to improve measurably. Sometimes that’s a classic optimization model, sometimes machine learning to estimate times, sometimes a well-built spreadsheet. The tool is chosen after understanding the problem, not before.
What data is needed?
Routings or operations per order, approximate times (estimates are fine) and the shift calendar. With that, a first model can be built to see how much headroom there is.
How much do I gain?
It depends on the headroom: it’s estimated before committing, by running the model on your historical data and comparing against what you actually did. If the headroom doesn’t justify the project, I’ll tell you and that’s the end of it.