Use demand forecasts to review inventory decisions

Planners reconcile inconsistent demand forecasts, stockouts and excess stock. The executive team needs to understand what drives an inventory change before committing working capital. Sales history can hide lost demand during stockouts. Promotions, new items and supplier delays change the planning problem, and a language model's explanation cannot replace a validated forecasting method.

We build a forecast review that uses validated methods for the numbers and AI for the explanation.

• The system validates the demand history and marks stockouts or missing periods, using historical demand, stock availability, promotions and lead times known at each forecast date.
• Forecasts come from an appropriate statistical or predictive method and are compared against a simple baseline, preserving what was known at each decision date.
• Inventory constraints are applied, then AI explains exceptions and prepares questions for planners.
• Insufficient history shows an explicit baseline or review state. A missing lead time stops replenishment suggestions. A changed supply constraint triggers replanning rather than a silent order.
• SKU, site and supplier records are limited by responsibility. Forecast calculation is separate from order permissions, and we document links to planning and procurement systems outside the model boundary.

• A forecast comparison. Forecasts by item and horizon, compared against a simple baseline using the information available at each date.
• An exception and scenario view. Inventory exceptions explained in plain language, with questions prepared for planners.
• A planner-approved recommendation. The final recommendation and its stated assumptions, approved by a planner.

• Planners verify special events and approve any overrides or ordering suggestions.
• Finance and operations agree the tradeoff between service, stock and working capital.
• Actual procurement remains an authorized system action.

Use the existing planning platform when it handles forecasts and workflow well. Custom AI fits the gaps in evidence, exception explanation and integrated review, and it is not presumed to improve forecast accuracy.

• Build a Constrained ML Deployment from a Measurable Baseline