Demand Planning and Forecasting Course

Why this module matters: Many forecasting disputes are actually process-design disputes. Teams argue about the number when they have never agreed the forecast level, time horizon, ownership or decision rules. Participants learn to:

• distinguish demand planning, demand management and statistical forecasting
• define what the demand plan is used for across supply, inventory, finance and commercial teams
• choose forecast level: item, item-location, customer, channel or product family
• define planning horizon and time buckets
• understand forecast lag and why accuracy depends on when the forecast is frozen
• set time fences and change-control rules
• define roles for demand planning, sales, marketing, finance and supply
• establish one consensus-demand process
• define which assumptions must accompany the number
• build a monthly demand-planning calendar.

Workshop: Design the demand-planning operating cycle for a sample business.

Why this module matters: Historical sales are not the same as historical demand. Stockouts, lost sales, promotions, one-off orders and supply constraints can all corrupt the signal before the model sees it. Participants learn to:

• distinguish shipment/sales history from unconstrained demand
• identify missing, abnormal and censored demand history
• correct for stockouts and known exceptional events
• identify level, trend, seasonality and intermittency
• segment items by demand behaviour
• choose simple baseline methods appropriately
• understand moving average, weighted average and exponential-smoothing logic at an applied level
• avoid overfitting with unnecessary model complexity
• establish a statistical baseline before applying judgment
• document data adjustments so the process remains auditable.

Workshop: Clean a demand-history dataset and choose baseline methods for different product patterns.

Why this module matters: A single accuracy percentage can create the wrong behaviour if the metric, denominator, hierarchy or lag is poorly chosen. Bias can be even more damaging because persistent over- or under-forecasting quietly drives inventory and service problems. Participants learn to:

• define forecast error and compare common accuracy approaches
• understand practical strengths and weaknesses of MAPE and related measures
• measure bias separately from absolute error
• choose the level at which performance should be reviewed
• avoid averaging metrics in mathematically misleading ways
• compare model forecast versus final consensus forecast
• measure Forecast Value Added from overrides and process steps
• identify persistent planner, customer, product or channel patterns
• separate random variation from systematic process failure
• build a monthly forecast-performance review.

Workshop: Diagnose forecast performance and identify which process steps add or destroy value.

Why this module matters: The hardest demand-planning cases are exactly the ones where history is least trustworthy. Judgment is necessary, but unmanaged judgment can destroy a good baseline faster than any statistical model. Participants learn to:

• use commercial intelligence without turning forecasting into opinion
• require reason codes and evidence for material overrides
• evaluate whether overrides improved the forecast after the event
• forecast promotions using comparable-event logic
• separate base demand from incremental promotional uplift
• approach new-product forecasting using analogues, ranges and staged learning
• manage product phase-in and phase-out
• identify demand that can be shaped when supply is constrained
• distinguish real demand sensing from reacting to short-term noise
• create scenario ranges where uncertainty is genuinely high.

Simulation: Resolve a forecast dispute involving a promotion, constrained supply and conflicting commercial assumptions.

Why this module matters: Demand planning creates value only when the rest of the business can plan against it. The monthly process needs to convert data and market intelligence into a single approved view of expected demand and its risks. Participants learn to:

• prepare the demand review around exceptions rather than every SKU
• reconcile sales, marketing and planner assumptions
• distinguish base forecast, opportunities, risks and management actions
• define approval and escalation rules for disputed forecasts
• connect demand scenarios to supply and capacity consequences
• prepare demand inputs for S&OP/IBP
• communicate uncertainty without weakening accountability
• monitor customer service, inventory and forecast performance together
• maintain assumptions between planning cycles
• continuously improve the planning process as products, markets and systems change.

Capstone: Present a complete monthly demand review pack with forecast, assumptions, risks, opportunities and S&OP handoff.