DECISION INTELLIGENCE ยท SCENARIO MODELING
Test the move before you make it.
Model a change on the operation's own data. Follow it through labor, throughput, cost to serve. See both paths and their projected outcomes before either one is committed.
DECISION INTELLIGENCE ยท SCENARIO MODELING
Model a change on the operation's own data. Follow it through labor, throughput, cost to serve. See both paths and their projected outcomes before either one is committed.
Both paths are visible: absorb the overtime or add a shift and spread the hours.
Revenue, labor allocation, and operational cost connected by account. Margin computed directly from the data.
Staffing, space, and throughput projected under three volume assumptions. The risk visible before the commitment is signed.
Blended rates, overtime exposure, and ramp time compared across both paths.
Carrying costs, rebalancing scenarios, and the margin impact of each path, visible before any inventory moves.
The effect on cost to serve, labor scheduling, and customer margin recalculated against the delayed timeline.
Shift the demand curve two weeks earlier. The recalculated labor coverage, overtime, and cost to serve appear at the new timeline.
Run margin through the change. The break point is visible.
Change an input and trace the effect through every connected metric. See how each change moves the outcome before anything is committed.
Run two or more paths side by side on the same data. The differences are visible across every metric they affect. Compare scenarios in a format that holds up in a board meeting, a lender review, or an operating partner's diligence: the paths, the projected outcomes, the variables that changed, and the ones that held. No separate deck required.
Change one metric and see every other metric that moves with it, and how strongly. Leadership sees the full ripple before the decision is made.
Run multiple projection methods against the data and see which one fits the business best. The forward deployed analyst validates the fit and adjusts where the data alone falls short. The projection carries forward as the read that actually matches how the operation moves.
Set a target: a margin floor, a cost-per-unit ceiling, a revenue threshold. Work backward to see which inputs need to change and by how much.
Five Forecasting Engines
The forward deployed analyst validates the fit and adjusts where the data alone falls short.
AI is what makes these five modes run at the speed of a conversation rather than a two-week analyst project.
Labor cost per unit at Warehouse 2 hit $1.42 last period. The threshold is $1.30. In a spreadsheet, that is a red cell.
In the model, the same number becomes the starting point for two scenarios. Hold the current shift structure, absorb the cost, and project where the number goes next period. Or restructure the weekend shift, adjust the agency mix, and project a path below the threshold.
Each scenario draws from the available history at that location, current agency rates, and the overtime structure on the ground. Both paths sit side by side. See the projected outcomes and the variables that drive each one.
The executive picks a path, and the decision goes on the record with its full reasoning.
(Demo-tenant display values. Not a customer result.)
One decision. A forward deployed analyst models it on the operation's real data during a four-week Sprint. The same analyst carries forward after.
Email one line with the decision you are weighing. It goes to the person who would run your Sprint, and they come back within one business day.