Description

This curriculum spans the technical, operational, and organizational dimensions of deploying machine learning in demand planning, comparable in scope to a multi-phase advisory engagement that integrates data engineering, model governance, and cross-functional workflow redesign within a large-scale supply chain environment.

Module 1: Foundations of Demand Planning in Business Contexts

Define demand planning scope across product hierarchies, including decisions on granularity (SKU-level vs. product family) based on data availability and business impact.
Select forecasting horizons (short-term vs. long-term) in alignment with supply chain lead times and financial planning cycles.
Map demand drivers such as promotions, seasonality, and market trends to historical data sources, ensuring traceability and auditability.
Establish cross-functional alignment between sales, finance, and supply chain on forecast ownership and change control processes.
Document data lineage from source systems (ERP, CRM) to forecasting models, including transformation rules and exception handling.
Implement version control for demand assumptions to support scenario analysis and executive review cycles.

Module 2: Data Engineering for Forecasting Systems

Design data pipelines that reconcile transactional data from multiple systems while handling latency, duplicates, and missing periods.
Apply outlier detection and correction rules for demand spikes due to one-time events, balancing statistical rigor with business context.
Implement temporal alignment of causal variables (e.g., marketing spend, weather) with demand outcomes, accounting for lag effects.
Construct feature stores to standardize input features across multiple forecasting models and reduce reprocessing.
Evaluate trade-offs between real-time data ingestion and batch processing based on forecast refresh requirements and infrastructure costs.
Enforce data quality SLAs with automated monitoring for drift, missing values, and schema changes in upstream systems.

Module 3: Statistical and Machine Learning Model Selection

Compare performance of classical time series models (e.g., ETS, ARIMA) against ML approaches (e.g., XGBoost, LSTM) using holdout periods and business-relevant error metrics.
Select model families based on data sparsity, with hierarchical forecasting for low-volume SKUs and regression models for promotional forecasting.
Balance model complexity against interpretability when presenting forecasts to non-technical stakeholders in finance and operations.
Implement regularization strategies to prevent overfitting on promotional or event-driven demand patterns.
Quantify forecast uncertainty using prediction intervals, and integrate confidence levels into inventory policy decisions.
Manage model decay by scheduling retraining cadences tied to data refresh rates and business cycle changes.

Module 4: Integration of Causal and External Variables

Incorporate promotional calendars into models using dummy variables or continuous spend metrics, adjusting for cannibalization and halo effects.
Evaluate the incremental lift of advertising campaigns by isolating demand signals from baseline trends using regression discontinuity.
Integrate macroeconomic indicators (e.g., CPI, unemployment) into long-range forecasts with lagged effects and threshold-based sensitivity.
Assess the reliability of third-party data sources (e.g., weather, foot traffic) and define fallback mechanisms during outages.
Model competitor actions using proxy signals (e.g., price changes, online mentions) while maintaining legal and ethical compliance.
Control for supply-side constraints (e.g., stockouts, allocation) when estimating true demand from observed sales data.

Module 5: Hierarchical and Unconstrained Forecasting

Design hierarchical aggregation structures (e.g., region > product category > SKU) that align with organizational reporting and planning levels.
Apply reconciliation methods (e.g., bottom-up, top-down, optimal combination) to ensure forecasts are consistent across levels.
Implement unconstraining logic to estimate latent demand during stockout periods using imputation or survival analysis.
Track forecast bias at different hierarchy levels to identify systematic over- or under-prediction by segment.
Allocate forecast overrides at summary levels to detailed SKUs using proportional or gravity-based distribution rules.
Manage computational load in high-dimensional hierarchies by prioritizing reconciliation on key product families or channels.

Module 6: Model Governance and Lifecycle Management

Establish model inventory with metadata including ownership, input features, performance history, and deployment status.
Define escalation paths for forecast exceptions exceeding predefined error thresholds or business impact criteria.
Conduct periodic model reviews to retire underperforming models and document reasons for continued use of legacy approaches.
Implement access controls and audit logs for forecast adjustments to ensure compliance with financial reporting standards.
Standardize model validation protocols across teams, including backtesting, cross-validation, and holdout evaluation.
Coordinate model deployment with change management processes to minimize disruption to downstream planning systems.

Module 7: Deployment and Integration with Business Systems

Design API contracts between forecasting engines and ERP systems to support automated forecast ingestion and error handling.
Map forecast outputs to inventory policy parameters (e.g., safety stock, reorder points) in alignment with service level targets.
Implement feedback loops to capture forecast accuracy post-execution and feed results into model improvement cycles.
Synchronize forecast release calendars with S&OP meetings and financial closing schedules to support decision timeliness.
Handle version mismatches between forecast models and downstream systems through data transformation layers or middleware.
Monitor system performance under peak load (e.g., month-end forecasting runs) and optimize for latency and throughput.

Module 8: Change Management and Organizational Adoption

Identify key stakeholders in demand planning workflows and define their data access, override rights, and approval responsibilities.
Develop training materials tailored to user roles (e.g., planner, supply chain manager, executive) focusing on interpretation and actionability.
Implement change tracking for manual forecast overrides to analyze patterns and reduce bias over time.
Establish KPIs for forecast adoption, including override frequency, model usage rate, and forecast error reduction.
Facilitate calibration sessions between sales and operations to align on assumptions and resolve forecast disagreements.
Design escalation workflows for forecast disputes, including documentation requirements and resolution timelines.