Description

This curriculum spans the design and governance of forecasting systems across technical and operational functions, comparable in scope to a multi-workshop program for establishing an enterprise-wide forecasting framework within a large organisation.

Module 1: Defining Forecasting Objectives and Scope

Selecting forecasting horizons (short-term vs. long-term) based on product lifecycle stage and strategic planning cycles.
Aligning forecast granularity (by product line, region, or customer segment) with supply chain and financial planning requirements.
Determining whether forecasts should support capacity planning, budgeting, or inventory management, and tailoring inputs accordingly.
Establishing data ownership and accountability across departments to prevent misaligned assumptions in cross-functional forecasts.
Deciding whether to produce single-point forecasts or probabilistic ranges based on risk tolerance and downstream system constraints.
Negotiating forecast ownership between technical management and business units when conflicting priorities arise over forecast assumptions.

Module 2: Data Collection, Validation, and Preprocessing

Integrating data from disparate systems (ERP, CRM, IoT sensors) while resolving schema mismatches and latency issues.
Designing automated data validation rules to detect anomalies such as zero-volume periods or sudden spikes due to system errors.
Handling missing data in time series using interpolation versus deletion based on pattern severity and volume.
Standardizing date formats and time zones across global operational units to ensure temporal consistency.
Applying outlier detection methods that distinguish between true demand shocks and data entry errors.
Documenting data lineage and transformation logic for auditability and stakeholder trust.

Module 3: Selection and Calibration of Forecasting Models

Choosing between exponential smoothing, ARIMA, and machine learning models based on data availability and forecast stability.
Calibrating model parameters using walk-forward validation to avoid overfitting to historical noise.
Deciding whether to use univariate versus multivariate models when causal factors (e.g., promotions, pricing) are unreliable.
Implementing model selection protocols that balance accuracy, interpretability, and computational cost.
Managing model decay by scheduling periodic retraining aligned with business cycle changes.
Configuring error metrics (MAPE, RMSE, WMAPE) based on business sensitivity to over- versus under-forecasting.

Module 4: Integration of Qualitative and Judgmental Inputs

Structuring expert elicitation sessions with engineering and sales teams to capture market intelligence not in historical data.
Quantifying subjective inputs (e.g., new product adoption estimates) using confidence intervals and scenario weights.
Implementing adjustment controls to prevent overruling statistical forecasts without documented rationale.
Designing consensus forecasting workflows that resolve conflicting inputs from regional managers.
Archiving judgmental overrides to audit bias patterns and improve future model design.
Defining escalation paths when technical leads and commercial teams disagree on forecast assumptions.

Module 5: Forecasting for New Products and Discontinuous Innovation

Selecting analogous products for baseline forecasting when historical data for new SKUs is unavailable.
Applying diffusion models (e.g., Bass model) to project adoption curves for technology-driven product launches.
Adjusting forecast inputs based on beta testing feedback while managing expectations of early adopters.
Integrating R&D milestone completion dates as leading indicators for go-to-market timing.
Managing forecast volatility during pilot production by using rolling forecasts with frequent recalibration.
Allocating safety stock based on forecast uncertainty bands rather than point estimates during initial rollout.

Module 6: Cross-Functional Alignment and Forecast Governance

Establishing a Sales & Operations Planning (S&OP) cadence that synchronizes technical forecasts with financial targets.
Defining version control protocols for forecast files to prevent conflicting use of outdated models.
Creating escalation thresholds for forecast variance that trigger root cause analysis by technical teams.
Assigning change management responsibility when forecast methodology updates impact downstream systems.
Documenting assumptions and constraints in forecast reports to support audit and regulatory compliance.
Implementing access controls to prevent unauthorized modifications to forecast models or input data.

Module 7: Performance Monitoring and Continuous Improvement

Tracking forecast accuracy by product tier and demand pattern to identify systemic model weaknesses.
Conducting root cause analysis on forecast errors tied to supply disruptions or unplanned engineering changes.
Updating forecasting models in response to structural shifts such as market exits or regulatory changes.
Integrating forecast performance dashboards into operational review meetings for accountability.
Rotating model ownership among data science team members to reduce dependency on individual expertise.
Conducting post-mortems after major forecast failures to refine processes and prevent recurrence.

Module 8: Scaling Forecasting Systems and Automation

Designing modular forecasting pipelines that support reuse across business units with minimal reconfiguration.
Implementing API-based model serving to integrate forecasts into inventory and capacity planning tools.
Choosing between centralized and decentralized forecasting architectures based on organizational autonomy.
Automating data ingestion and model retraining workflows while maintaining rollback capability.
Scaling compute resources for batch forecasting jobs during peak planning periods without degrading system performance.
Enforcing model versioning and deployment standards to ensure reproducibility across environments.