Demand Management Training Course

Participants can assess demand-data fitness and specify controls for planning data.

• Sales history, orders, shipments and consumption as alternative demand signals
• Master-data fields governing product, customer, location and unit consistency
• Data-cleansing rules for duplicates, missing values and invalid transactions
• Calendar alignment for trading days, holidays, seasons and fiscal periods
• Outlier classification separating data errors, events and genuine demand changes
• Data-quality scorecard covering completeness, timeliness, accuracy and consistency

Participants can segment demand and assign differentiated planning policies.

• ABC analysis ranks items by revenue, margin, volume or strategic importance
• XYZ analysis classifies demand variability using coefficient-of-variation thresholds
• ABC-XYZ matrix assigns forecast effort, review frequency and inventory treatment
• Customer segmentation distinguishes contractual, strategic, transactional and volatile demand
• Portfolio lifecycle status identifies launch, growth, mature and phase-out items
• Planning-policy matrix documents forecast method, ownership and exception thresholds

Participants can identify demand patterns and select suitable baseline forecast methods.

• Level, trend, seasonality and intermittency identified from historical demand plots
• Moving averages smooth short-term variation in stable demand series
• Simple exponential smoothing applies weighted emphasis to recent observations
• Holt-Winters forecasting models level, trend and seasonal demand components
• Seasonal indices convert recurring patterns into period-specific forecast factors
• Croston method addresses intermittent demand with irregular nonzero consumption

Participants can build a baseline forecast and apply controlled commercial adjustments.

• Baseline forecast generated from cleansed history and selected statistical method
• Judgemental overrides recorded with owner, rationale, value and expiry date
• Sales intelligence evaluated for evidence, customer commitment and timing certainty
• Consensus forecasting reconciles inputs from sales, marketing, finance and operations
• Assumption register separates known facts, estimates, risks and unresolved questions
• Forecast freeze windows protect supply execution from avoidable late changes

Participants can measure forecast performance and diagnose sources of error.

• MAPE measures percentage error while excluding zero-demand calculation distortions
• WAPE compares total absolute error against total actual demand
• MAD and tracking signal identify persistent forecast bias patterns
• Forecast bias distinguishes systematic over-forecasting from under-forecasting behaviour
• Forecast Value Add compares each planning intervention against statistical baseline
• Error segmentation isolates performance by product family, customer and horizon

Participants can use near-term signals to manage demand changes without destabilising supply.

• Demand sensing combines recent orders, shipments, point-of-sale and market signals
• Short-horizon forecast updates apply defined cut-off times and change tolerances
• Exception-based planning prioritises material deviations from agreed demand plans
• Order-pattern analysis identifies pull-forwards, cancellations, spikes and allocation effects
• Available-to-promise signals distinguish confirmed demand from tentative customer requests
• Near-term control tower routines assign response actions and escalation ownership

Participants can plan promotional demand and measure event outcomes accurately.

• Promotion calendars capture mechanics, timing, channels, products and customer commitments
• Incremental uplift separates promotion-driven demand from baseline sales expectations
• Cannibalisation analysis identifies volume shifted between products, channels and periods
• Forward buying and pantry loading assessed before approving promotional volumes
• Post-event review compares planned uplift, actual sales, margin and residual stock
• Promotion approval gates test supply feasibility, inventory exposure and financial return

Participants can develop demand assumptions for launches, substitutions and phase-outs.

• Analogous-product forecasting uses comparable launches, markets and customer profiles
• Bass diffusion concepts estimate adoption patterns for genuinely new offerings
• Launch assumptions document distribution build, listings, price and marketing support
• Substitution rules prevent double counting between successor and retiring products
• Phase-out demand plans manage last-buy decisions, residual inventory and obsolescence
• Lifecycle review triggers revise planning parameters as adoption evidence accumulates

Participants can establish structured customer collaboration and commitment controls.

• CPFR principles structure joint planning, forecasting and replenishment conversations
• Customer forecast agreements define horizons, update frequency and accountability
• Commitment classifications separate firm orders, forecasts, nominations and opportunities
• Joint exception reviews address material demand changes and root causes
• Information-sharing protocols protect commercially sensitive customer and market data
• Supplier-managed inventory arrangements clarify demand visibility and replenishment responsibilities

Participants can connect demand variability to inventory targets and service commitments.

• Safety-stock calculations reflect demand variability, lead-time uncertainty and target service
• Reorder points combine expected demand during lead time with safety stock
• Service-level definitions distinguish fill rate, cycle service and order completeness
• Demand variability informs inventory buffers for each ABC-XYZ segment
• Decoupling points determine where forecast-driven replenishment becomes customer-order driven
• Inventory-policy review balances working capital, availability and obsolescence exposure

Participants can prepare and facilitate a demand review for S&OP or IBP.

• Demand-review agenda structures performance, assumptions, risks, decisions and actions
• One-number plan reconciles commercial forecast, financial outlook and operational demand
• Pre-S&OP packs identify unresolved gaps requiring cross-functional decisions
• Executive S&OP decisions address demand priorities, trade-offs and contingency actions
• Integrated Business Planning extends S&OP through financial and strategic alignment
• Meeting discipline records decisions, owners, deadlines and assumption changes

Participants can reconcile constrained supply with demand priorities using explicit rules.

• Rough-cut capacity planning tests aggregate demand against available operating capacity
• Supplier lead-time analysis identifies exposure from demand changes and replenishment delays
• Allocation rules prioritise customers, channels, contracts, margins and critical needs
• Available-to-promise logic commits demand against planned inventory and receipts
• Constraint scenarios quantify backlog, lost sales, expediting and revenue-at-risk
• Demand-shaping actions include substitutions, pricing, allocations and delivery-date changes

Participants can construct demand scenarios and define risk-response triggers.

• Base, upside and downside scenarios quantify plausible demand range assumptions
• Sensitivity analysis tests volume, mix, timing and customer-loss impacts
• Risk register captures triggers, probability, impact, owner and response actions
• Early-warning indicators monitor market, customer, competitor and regulatory demand signals
• Decision trees compare contingency options under uncertain demand outcomes
• Scenario thresholds trigger procurement, capacity, inventory or commercial interventions

Participants can specify functional requirements and controls for demand-planning technology.

• Planning-system workflow supports baseline generation, overrides, approvals and audit trails
• Dashboard design links forecast accuracy, bias, service and inventory measures
• Machine-learning forecasts require explainability, governance and performance comparison
• Data integration connects ERP, CRM, point-of-sale and external market sources
• Exception alerts use tolerances, materiality thresholds and accountable response owners
• Spreadsheet controls protect formulas, versions, source data and approval records

Participants can plan a practical demand-management improvement programme with measurable controls.

• Current-state assessment scores process maturity, data quality, skills and governance
• Future-state operating model defines roles, cadence, inputs, outputs and controls
• KPI tree links forecast measures to service, inventory, cost and financial outcomes
• Implementation roadmap sequences quick wins, capability building and system changes
• Change-impact assessment identifies affected roles, behaviours, procedures and incentives
• Benefits-tracking plan assigns baselines, targets, owners and review dates

Participants can build, review and present a complete demand-management operating pack for a defined business case.

• Demand-data assessment documents quality issues, corrections and remaining limitations
• ABC-XYZ segmentation assigns planning policies for the selected product portfolio
• Forecast model combines baseline, assumptions, overrides and scenario ranges
• Performance scorecard reports accuracy, bias, service, inventory and exceptions
• S&OP demand-review pack presents decisions, risks, actions and accountable owners
• Implementation roadmap prioritises governance, data, capability and technology improvements