SolutionsPlanningInventory & Planning

Stock Reservation

Two channels want the same units, and one launch can't be short.

The decision

Inventory just landed. A launch needs it reserved, a key wholesale account is asking for the same SKU, and DTC is selling it live right now. Reserve for the launch, release to wholesale, or let DTC keep flowing?

Why it's hard today

Reserve too much and you starve live demand; reserve too little and the launch gets embarrassed. The call depends on launch firmness, account importance, and real-time sell-through — all moving targets.

Why your systems don't help

Your OMS shows available-to-promise; it doesn't know the launch is firm, the account is strategic, or that DTC velocity just spiked. It allocates by rule, not by judgment.

What AgentForgeOS does

It assembles the picture you can't see, and makes the call under your rules.

Reserve what the launch firmly needs, release a partial to the tier-A account, and cap DTC at the remainder — under your reservation policy.

Operational context assembled
  • Live sell-through by channel
  • Launch commitments and dates
  • Account tier and open orders
  • On-hand and inbound
  • Reservation aging
Governed by your policy
  • Launch reservations honored first
  • Strategic-account minimums
  • DTC velocity caps
  • Reservation expiry
decision-workspace · reserve · SKU 3318

⚠ Contention — SKU 3318 wanted by launch, wholesale, and DTC

Evidence assembled

  • Launch firm in 9 days, 800 units
  • Tier-A wholesale PO open
  • DTC velocity +40% today
  • Inbound covers ~half the gap

Recommendation · reserve launch, partial-release wholesale, cap DTC

Policy: launch protected · tier-A minimum met · awaiting planner

Decision Object #3318Evidence ×4Policy ✓ApproveAdjust

What improves

  • Launches that don't run short
  • Key accounts kept whole
  • Less idle reserved stock
The knowledge it keeps

Every reservation and its result is kept, so the model learns which launches actually pull and which reservations expire, tightening with each cycle.

Under the hood

Underneath, this is the same operating model the rest of the platform runs: verified context is assembled, options are weighed and adversarially challenged, the decision is governed by your policy, and the outcome is learned. The decision changes from one of these to the next. The architecture does not.

See how it works

This is exactly how your team works today.

Only now the decision is assembled, governed, and remembered — instead of made from memory and lost.