Build a Governed AI Forecast Review Workflow Before Connecting AI to Your Planning System

Objective

Create a controlled Demand Planning workflow in which an AI model proposes forecast adjustments while planners retain ownership of approval and publication.

Target user

Demand planners, S&OP managers and planning-data owners.

Inputs

• Historical demand
• Baseline forecast
• Forecast accuracy and bias
• Promotions and events
• Planner overrides
• Product hierarchy
• Planning horizon

Architecture

Use a sandboxed AI layer between approved planning data and the APS or ERP. The AI may recommend changes, but only approved versions are published.

Steps

• Select a limited group of stable SKUs.
• Generate or import a statistical baseline forecast.
• Ask the AI layer to explain anomalies and propose adjustments.
• Record the changed input, recommendation and expected impact.
• Require a planner to accept, modify or reject the proposal.
• Store the approved forecast as a new version.
• Compare the approved forecast with the baseline using Forecast Value Added.
• Publish only approved versions into APS, ERP or BI.

Validation

• Every adjustment cites supporting data.
• The recommendation can be reproduced.
• The baseline is preserved.
• Accepted and rejected overrides are retained.
• The workflow can be stopped without affecting production planning.

Governance box

• Data source: approved demand history and business events.
• Owner: demand-planning manager.
• Validation: accuracy, bias and FVA.
• Version control: baseline, AI proposal and approved plan.
• Access: AI recommends; planner approves.
• Manual override: mandatory.
• Failure mode: revert to the approved statistical baseline.