Manufacturing · Production planning AI
Your MRP runs the schedule. Your schedule never quite fits the line.
Banao builds AI-driven production planning that accounts for real constraints — machine capacity, tooling changeover, raw-material availability, and order priority — not just the demand signal your ERP sees.
The output is a feasible sequence your floor team can execute: not a theoretical plan that breaks before Monday's first shift.
The first call is free · 45 minutes · no obligation
What we build
What a Banao production planning deployment covers
Production planning failure points are well-known — idle time, late orders, excess WIP. We address each with a model built around your operation, not a generic scheduler.
Finite capacity scheduling
A constraint-aware model that sequences orders against real machine capacity, tooling availability, and crew shifts — not the infinite-resource assumption most MRP runs on.
Changeover and sequence optimisation
For lines where changeover time depends on product sequence, the model minimises total changeover cost without sacrificing on-time delivery — a combinatorial problem too large for manual planning.
Live re-scheduling on disruption
When a machine goes down, a rush order arrives, or material is short, the planner re-runs in minutes — not next morning — and hands the floor a revised sequence against the current state.
ERP and MES integration
The scheduling layer reads from your existing ERP and MES — SAP, Oracle, Dynamics, or custom — rather than replacing them, so it runs on the same data your procurement and logistics teams already trust.
WIP buffer and bottleneck signal
The model surfaces which WIP buffers are about to block downstream operations, giving planners a four-to-twelve-hour warning before the crisis — not a post-shift report.
Planner override and confidence scoring
Planners override any machine or sequence call. The model scores its own confidence on each decision so planners know where to focus, and every override correction feeds back into the next planning run.
Receipts
Where this has run
Metrics are being validated in our case-study pack and will publish once verified. Client details anonymised at client request.
AI scheduling replaced spreadsheet-based daily planning
Daily scheduling was built on a planner's spreadsheet and local knowledge. Banao built a finite-capacity model integrated with the ERP order book, replacing the spreadsheet with a system that re-plans on each new order or disruption.
Dogfooding
We run our own operation on AI before we sell it
Banao manages a ~300-person engineering company on its own AI products. Our internal demand, staffing, and project scheduling run on the same kind of constraint-aware planning logic we deploy for manufacturers.
A planning system that has to hold up in our own operation is debugged before it reaches your floor. We are not recommending AI scheduling from the outside.
Runs Banao's demand-gen pipeline against live capacity constraints.
Schedules and screens engineering hires at scale for Banao's own teams.
The honest version
When AI production planning is the wrong investment
A scheduling model is not always the binding constraint. We will tell you before you build one:
- Poor ERP data quality means the model optimises bad inputs. Data remediation comes first — we scope this in the Discovery Sprint.
- Low-variant lines with long stable runs often get adequate scheduling from simple rules. A model earns its cost when variability is high and changeover cost is material.
- If the real bottleneck is procurement lead time or machine age, scheduling optimisation may hide that for a while rather than fix it.
How we start
How we start — see the gap before you commit
We do not quote a scheduling system against a spec sheet. We audit your actual order flow and line data first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We audit a sample of your order history, machine logs, and current plan-vs-actual data, identify where schedule breaks are costing the most, and hand back a feasibility report and ROI model — yours to keep regardless of next steps. If you proceed to build, the Sprint fee is credited.
- 02
Build
We build and integrate the scheduling model into your ERP and MES data feeds, validate it against historical order cycles, and calibrate with your planning team before go-live.
- 03
Production & iteration
Live planning with your floor team, override logging, and a fortnightly model review cycle for the first quarter. Planners get a confidence-scored output they can act on rather than a black box.
FAQ
Frequently asked questions
Does this replace our ERP's planning module?
No. It sits alongside your ERP and reads from it. The AI scheduling layer handles the finite-capacity sequencing problem that most ERP schedulers approximate poorly — it does not replace order management, procurement, or MRP.
How long does a production planning model take to go live?
The Discovery Sprint is two weeks. A production-ready model integrated with your ERP and MES is typically eight to sixteen weeks from Sprint sign-off, depending on data readiness and integration complexity.
How many variants or SKUs can it handle?
The model handles hundreds of variants and thousands of orders in the planning horizon. We tune the combinatorial search to your responsiveness requirement — seconds for on-the-fly replanning, minutes for full horizon re-optimisation.
What if our schedule changes many times a day?
High-disruption lines are exactly where constraint-aware AI earns its cost. The model is designed to re-plan on demand, not just overnight. Rush orders, downtime, and material shortages all trigger a re-sequence against the current floor state.
How do we know the schedule it produces is actually feasible?
Feasibility constraints — machine capacity, tooling, crew shifts, changeover, material availability — are explicit inputs to the model, not assumptions. The planner confidence score flags where the model had to make trade-offs, so the planning team can inspect the hardest calls before they reach the floor.
Get started
Bring your worst scheduling week
Share your order log and a week where scheduling broke down. In 45 minutes we will tell you whether AI production planning would have caught it — and what it would cost to prevent it.
Book a Discovery Sprint