This process guides the organization from an objective baseline forecast to an executive-approved plan.
Each step is:
Owned by a defined role
Captured under a forecast type
Locked with a freeze tag
This ensures clarity, traceability, and alignment across all functions. With clear roles, structured reviews, and version control, the S&OP cycle reduces silos and drives the business toward common goals.
Role: System (Pawa ETL / AI) + Data Steward
Forecast Type: AI Forecast (optional)
Action:
Collect, clean, and validate inputs (POS, Sell-In, Inventory, PO, Master Data).
Pawa can generate an AI Forecast as a benchmark to compare against human forecasts.
Freeze Tag: Data Import – AI Forecast
Outcome: Reliable data foundation and, if enabled, an AI-driven forecast to guide the baseline.
Role: Planner / Demand Analyst
Forecast Type: Planner Forecast
Action: Pawa auto-imports POS, Sell-In, Inventory, and PO data (if ERP supports it). The Planner applies only high-level adjustments.
Freeze Tag: Revision 1 – Planner
Outcome: Objective baseline forecast.
Role: Key Account Manager (KAM)
Forecast Type: KAM Forecast
Action: Review the Planner Forecast, adjust for promotions, contracts, and customer insights, and add commentary.
Freeze Tag: Revision 2 – KAM
Outcome: Sales- and market-informed forecast.
Role: Cross-functional team (Planner + KAM + Finance + Supply Chain)
Forecast Type: Reconciliation Forecast
Action: Managers challenge and validate Planner and KAM inputs.
Freeze Tag: Revision 3 – Reconciliation
Outcome: Cross-functional consensus.
Role: Executives (GM, Finance, Supply Chain, Sales Leadership)
Forecast Type: Committed Forecast
Action: Present the reconciliation forecast to senior management. Discuss, adjust if needed, and upon approval, freeze the version.
Freeze Tag: Day 3 – Committed
Outcome: Final executive-approved plan.
Step | Role(s) | Forecast Type | Freeze Tag | Outcome |
1. Data Preparation & Pawa AI Forecast (add-on) | System (Pawa ETL/AI), Data Steward | AI Forecast (optional) | Data Import – AI Forecast | Clean data & AI benchmark |
2. Planner Demand Forecast | Planner / Demand Analyst | Planner Forecast | Revision 1 – Planner | Objective baseline |
3. KAM Demand Forecast | Key Account Manager | KAM Forecast | Revision 2 – KAM | Sales- and market-informed |
4. Reconciliation Forecast | Planner + KAM + Finance + Supply Chain | Reconciliation Forecast | Revision 3 – Reconciliation | Cross-functional consensus |
5. Executive Review & Committed Forecast | Executives (GM, Finance, Supply Chain, Sales Leadership) | Committed Forecast | Day 3 – Committed | Final approved plan |
Clear version control – Each stage is locked and labeled (Data Import/AI, Revision 1 – Planner, Revision 2 – KAM, Revision 3 – Reconciliation, Day 3 – Committed).
Traceable history – Provides an easy audit trail to compare forecasts across stages and understand where assumptions evolved.
Strong governance – Eliminates ambiguity by making it clear which version is the official, approved forecast.
