Hub for Available-to-Promise

Commercial teams commit to delivery dates using a number the planning system produced overnight, which means they are quoting yesterday's network. When the date slips, the reason is usually a single constraint several tiers away that nobody could see from the sales screen — and by the time it surfaces, the mitigation window has closed. The alternative behaviour is worse: sales quotes conservatively, and the organisation leaves revenue on the table protecting itself from a number it does not trust.

• Available-to-promise is a batch figure, so it is stale at exactly the moment it is quoted.
• The specific constraint limiting ATP is invisible from the commercial screen.
• Mitigations — expedite, substitute, reallocate — are evaluated after the commitment, not before it.
• Distrust of the number produces conservative quoting, which costs revenue quietly.

Hub computes available-to-promise continuously across the network rather than as an overnight batch, so the figure a commercial team quotes is the current one. Behind the number, the constraint chain is exposed: teams can deep-dive from a weak ATP position to the specific root cause holding it back, at whichever tier it sits. Mitigations are then tested rather than guessed — the platform simulates the effect of a purchasing action, a customer moved onto a valid alternative, or capacity reallocated, and reports the resulting ATP change before anything is committed. Opportunity alerting runs the same logic in the other direction, surfacing actions that would open up availability on high-demand products before anyone asks for them.

This is a search problem over a large, interdependent network, and the useful question is counterfactual: not what ATP is, but what it would become under a specific action. Enumerating those counterfactuals by hand is exactly what planners do not have time for, and a static report cannot do it at all. The model's role is to propose the mitigations worth simulating — informed by the constraint structure and by what has worked on comparable bottlenecks — so the simulation budget is spent on plausible actions rather than on the whole action space.

ATP

Real-time, network-wide, replacing an overnight batch figure

DIAGNOSIS

Root-cause deep-dive from position to constraining tier

SIMULATION

Mitigation scenarios scored before commitment

ALERTING

Proactive opportunities to increase ATP on high-demand products

AUTONOMY

Recommends and simulates; the commitment stays commercial

1. 01Available-to-promise derived in real time rather than read from an overnight run.
2. 02Network-level visualisation that resolves a weak position down to its actual constraint.
3. 03Mitigation scenarios test-run so the most effective action is known before it is taken.
4. 04Customers moved between valid alternatives to free capacity on constrained products.
5. 05Purchasing opportunities surfaced proactively where they would lift ATP.
6. 06Bottlenecks ranked by revenue impact, not by tier or by how loudly they were reported.

Real time

ATP, against an overnight batchdesign intent

Pre-commit

Mitigation testing, against post-hoc escalationdesign intent

• VOICE-FIRST ENTERPRISE OPERATING SYSTEMMivaanOS→
• RESTAURANT OPERATING SYSTEMPrime POS→
• CUSTOMER SERVICE PLATFORMHub Customer Service Engine→