Decision vectors & action vectors
Solya’s decision layer works in two steps. First it computes decision vectors: per variant-and-shop scores such as restock urgency, stockout risk, overstock risk, surplus/deficit, and transfer urgency — how risky or urgent is this position? Then it resolves those scores into action vectors: a concrete recommendation — so what should we do? — a quantity to reorder, units to transfer, or a discount percentage, each with a confidence and an explanation. These recommendations drive plan building, the alert workspace, the task inbox, and the score-driven workflow strategies — but they always propose; you decide.How decision & action vectors are computed
The full technical reference — inputs, per-domain scoring formulas and weights, resolution,
table schemas, and how the app consumes them.
Plan simulation
Simulation runs a plan against the decision layer without persisting anything. You pick an entity grain (brand / product / variant), an action family (restock / rebalance / markdown), a strategy, and optionally a ruleset; Solya returns the items it would add and with what quantities or discounts — along with the decision context and which rules applied. You can then add them all, cherry-pick, or discard.Sales forecasts
Forecasts predict future sales per variant / size / shop over a horizon, with confidence bounds. They feed risk assessment and stock planning, and are surfaced in analytics and as a chart type on dashboards.How to use them
1
Review recommendations
Recommended quantities/discounts appear when building plans and in the AI hub.
2
Simulate before committing
Use simulation to preview a strategy’s output on a scope, then accept what you want.
3
Lean on forecasts
Consult forecasts to size restock and pre-season plans.
Recommendations respect your rules: the decision layer reads the org’s ruleset (and
margin floors, budgets, etc.) when shaping its proposals. Items added from a
recommendation are tagged with their attribution in the
activity log.