Advanced Demand Planning Analyst

Skills at this level

Forecast Model Maintenance

— Creates and maintains statistical forecast models in the planning tool, tuning parameters and incorporating sales, marketing, finance, and syndicated consumer data (e.g., Nielsen) to keep the operational forecast current across SKUs and customers.

Multi-Level Demand Aggregation

— Builds operational demand forecasts at multiple levels of aggregation and across multiple time horizons, moving accurately between item, customer, and category views within the planning hierarchy.

SAP IBP Operation

— Loads historical sales data, runs statistical forecast runs, maintains item attributes, and manages exception thresholds within SAP Integrated Business Planning to produce and update the demand plan.

Forecast Accuracy & KPI Reporting

— Compiles and reconciles forecast-accuracy and inventory KPI reports, comparing quantity and financial forecasts to actuals and surfacing variances that need planner or cross-functional attention.

Forecast Variance Root-Cause Analysis

— Investigates recurring demand-plan variances to isolate their cause — promotional distortion, product transition, or model misfit — and adjusts forecast or inventory rules to correct them.

Product Lifecycle Forecasting

— Applies the correct forecast treatment to high runners, new-product introductions, substitutions, and end-of-life items, and communicates the resulting inventory strategy to Supply Planning.

S&OP Data Preparation & Dialogue

— Assembles the forecast data packet for the monthly S&OP cycle and leads the demand dialogue with Sales and Marketing counterparts, presenting numbers and the reasoning behind them.

Cross-Functional Input Reconciliation

— Bridges Sales, Marketing, Finance, and retailer replenishment analysts to gather and reconcile conflicting demand inputs into a single agreed forecast number.

Forecast Procedure & Configuration Development

— Defines new forecasting procedures, model-selection logic, and planning-tool configuration for the team, documenting them as the standard method and training peers on their use.

Statistical & Predictive Demand Analysis

— Uses historical data, statistical models, and AI-driven insights to predict future product demand and to build long-horizon or new-product forecasts where standard models do not apply.