Description

This curriculum spans the analytical rigor and cross-functional coordination typical of a multi-workshop operational diagnostics program, addressing the same supply-demand alignment challenges seen in enterprise-scale current state assessments.

Module 1: Defining Market Boundaries and Product Segmentation

Select whether to define the market based on functional substitutes or customer usage patterns when product categories overlap.
Determine the granularity of product segmentation by balancing data availability against strategic relevance for demand forecasting.
Decide whether to include gray-market or parallel imports in supply-side inventory assessments for pricing consistency analysis.
Resolve conflicts between internal product classification systems and external industry taxonomy when benchmarking market share.
Establish criteria for including or excluding bundled offerings when analyzing standalone product demand.
Assess geographic scope alignment between internal sales regions and external market reporting standards for accurate supply mapping.

Module 2: Demand Signal Capture and Data Integration

Choose between point-of-sale, shipment, or order data as the primary demand signal based on channel structure and lag effects.
Integrate disparate demand data sources by reconciling timing differences between distributor reports and direct customer transactions.
Implement rules for handling returns and cancellations in historical demand series to avoid distortion in trend analysis.
Decide whether to adjust demand data for known promotional spikes when estimating baseline consumption rates.
Address discrepancies between ERP order records and third-party syndicated data through automated reconciliation logic.
Design data pipelines that maintain auditability when aggregating demand signals across multiple business units.

Module 3: Supply Capacity Assessment and Constraint Mapping

Identify operational bottlenecks by comparing theoretical production capacity with actual output across multiple facilities.
Classify supply constraints as temporary (labor shortages) versus structural (permit limitations) for response planning.
Map supplier lead time variability to procurement contracts and assess implications for inventory buffer sizing.
Validate third-party capacity claims through on-site audits or production record sampling before inclusion in supply models.
Adjust nameplate capacity figures for planned downtime, maintenance schedules, and yield loss in utilization calculations.
Track dual-use production lines to determine reallocation feasibility during supply-demand imbalances.

Module 4: Inventory Positioning and Flow Analysis

Reconcile inventory records across distribution tiers to identify phantom stock or hidden reserves affecting supply visibility.
Calculate days of supply at each node using consistent cost bases and ownership transfer points to avoid double counting.
Determine whether consignment inventory should be treated as available supply based on access speed and control terms.
Trace inventory flow patterns to detect hoarding behavior or artificial stockouts in distributor networks.
Adjust safety stock levels based on observed replenishment cycle variability rather than policy defaults.
Identify obsolete stock within total inventory that should be excluded from active supply calculations.

Module 5: Price Elasticity and Behavioral Response Modeling

Estimate short-term versus long-term elasticity using historical price changes during supply-constrained versus unconstrained periods.
Isolate demand shifts caused by competitor pricing from those due to supply availability changes using regression controls.
Adjust elasticity models for customer contract terms that limit immediate response to spot market fluctuations.
Validate elasticity assumptions against actual behavior during recent stockout events or allocation periods.
Account for forward buying behavior in response to announced price increases when modeling demand surges.
Use panel data to differentiate between temporary substitution and permanent brand switching during supply gaps.

Module 6: Gap Quantification and Imbalance Diagnosis

Define acceptable tolerance bands for supply-demand alignment to distinguish noise from meaningful gaps.
Attribute unmet demand to supply constraints versus lost sales using customer inquiry and backorder data.
Adjust for seasonality in both supply and demand series before calculating structural imbalances.
Determine whether observed gaps stem from forecasting errors or actual capacity limitations using root cause tagging.
Quantify the portion of supply shortfall attributable to logistics delays versus production shortfalls.
Track gap persistence over time to differentiate cyclical mismatches from chronic structural deficits.

Module 7: Stakeholder Alignment and Cross-Functional Governance

Establish escalation protocols for resolving conflicts between sales targets and production capacity in quarterly planning.
Define ownership of supply-demand metrics to prevent misalignment between finance, operations, and commercial teams.
Implement change control for assumptions used in supply-demand models to ensure consistency across reports.
Coordinate data refresh schedules across departments to avoid decision-making based on mismatched timeframes.
Design exception reporting thresholds that trigger cross-functional reviews without generating alert fatigue.
Document rationale for strategic oversupply or undersupply decisions to maintain audit trail for compliance purposes.

Module 8: Scenario Planning and Forward State Simulation

Model the impact of supplier diversification on lead time variability and total available supply under disruption conditions.
Simulate allocation strategies during supply shortages to assess fairness, customer retention, and margin implications.
Test demand-shaping initiatives such as lead time quotes or volume incentives under different supply constraint levels.
Project inventory burn rates under alternative demand recovery scenarios following a supply disruption.
Assess the break-even point for capacity expansion by modeling payback under probabilistic demand uptake.
Integrate macroeconomic indicators into supply-demand simulations to stress-test assumptions under recession conditions.