1. Home
  2. Services
  3. AI workflow pilots
  4. Predictive inventory AI

Test a forecast against the way planners work today.

Use one product group, location group, or forecast horizon. Compare results with the current method and keep a planner between the recommendation and the operating system.

Scope an AI workflow pilot
Best for
Replenishment or planning decisions with a measurable current baseline
Starting point
One product category, location group, or forecast horizon
Typical pilot
One backtest and controlled operating trial
Decision at the end
Integrate, revise, narrow, or stop after the backtest and trial

Historical demand and operating context produce a versioned forecast. A planner reviews it before an approved update reaches planning or inventory systems.

Keep a planner between the forecast and the order.

Planning evidence

Forecast workflow

Reviewed decision

This forecast can be tested when

  • A planner owns the inventory decision and can explain the current method
  • Historical demand and operating context are available for comparison
  • The team agrees on the error measure and review step before the test

Not a fit: a forecast accuracy promise without usable history, a current method to beat, or a planner responsible for the decision.

How the engagement runs

Three steps, then a decision.

  1. Baseline

    Define the inventory decision, product scope, forecast horizon, available history, current method, and error measure.

  2. Backtest

    Compare candidate forecasts with the current method and review errors and exceptions with the planners.

  3. Decide

    Run a controlled trial with planner approval before choosing integration, revision, or stopping.

Bring us one inventory decision and the method planners use now.

We will define a fair backtest before any forecast reaches an operating system.

Scope an AI workflow pilot