Description

This curriculum spans the design and execution of integrated supply chain processes across planning, sourcing, manufacturing, and logistics, comparable in scope to a multi-phase operational transformation program involving data governance, system integration, and cross-functional process alignment in a global enterprise.

Module 1: Defining Process Boundaries and Stakeholder Alignment

Determine where supply chain process ownership begins and ends across procurement, manufacturing, and logistics in multi-divisional organizations.
Negotiate data-sharing agreements with third-party logistics providers to access real-time shipment tracking without violating contractual confidentiality clauses.
Map cross-functional handoffs between demand planning and production scheduling to identify duplicated effort or communication gaps.
Establish escalation protocols for conflicting priorities between sales forecasts and inventory carrying cost targets.
Define KPI ownership when metrics span departments—e.g., whether on-time delivery is managed by logistics or sales operations.
Document assumptions in process scope when integrating legacy ERP systems with new demand sensing tools.
Conduct stakeholder workshops to align on process improvement goals, ensuring regional supply chain leads are represented.
Identify shadow processes—manual spreadsheets or email-based approvals—that bypass official workflows but are critical to operations.

Module 2: Data Integration and Master Data Governance

Resolve SKU master data conflicts when multiple systems use different identifiers for the same product across regions.
Implement data validation rules at point of entry to prevent duplicate supplier records in procurement systems.
Design a golden record strategy for customer data when sales, CRM, and logistics systems maintain separate records.
Decide whether to reconcile inventory balances in real-time or batch mode given system latency and transaction volume.
Configure ETL pipelines to handle time zone differences in global transaction timestamps for accurate lead time calculations.
Enforce data ownership policies by assigning stewardship roles for critical fields like lead time, safety stock, and MOQ.
Assess data lineage when integrating IoT sensor data from warehouse equipment into planning systems.
Implement data quality dashboards that flag anomalies such as negative on-hand inventory or unrealistic order cycle times.

Module 3: Demand Sensing and Forecasting Integration

Select forecasting models based on product lifecycle stage—exponential smoothing for mature SKUs, new product forecasting for launches.
Integrate point-of-sale data from retail partners into demand planning systems while managing data latency and coverage gaps.
Adjust baseline forecasts for known disruptions such as trade promotions or plant shutdowns using event modeling.
Balance statistical forecast accuracy with sales team overrides, requiring documented justification for manual adjustments.
Deploy demand sensing algorithms that use shipment, warehouse withdrawal, and POS data to detect shifts faster than traditional forecasts.
Define forecast horizon granularity—daily for short-term production, monthly for long-term capacity planning.
Implement forecast error tracking by product segment to identify systematic bias in specific categories.
Manage consensus forecasting meetings with structured agendas to prevent dominance by loudest voice or highest rank.

Module 4: Inventory Optimization and Replenishment Logic

Set safety stock levels using probabilistic models that account for lead time variability and service level targets.
Configure min/max settings in warehouse management systems to reflect actual picking constraints and bin capacity.
Adjust reorder points dynamically when supplier performance degrades due to geopolitical or logistical disruptions.
Implement multi-echelon inventory optimization to coordinate stock levels between central DCs and regional warehouses.
Decide whether to use push or pull replenishment based on demand stability and transportation economics.
Model the cost of stockouts versus carrying costs when optimizing for working capital efficiency.
Integrate shelf-life constraints into replenishment logic for perishable goods in cold chain operations.
Validate ABC classification periodically to prevent outdated categorization from distorting inventory policies.

Module 5: Supplier Collaboration and Risk Mitigation

Negotiate VMI (Vendor Managed Inventory) agreements with key suppliers, defining data access, replenishment triggers, and liability.
Implement supplier risk scoring that incorporates financial health, geopolitical exposure, and logistics reliability.
Design dual-sourcing strategies for critical components, balancing cost increase against supply continuity.
Conduct on-site audits of supplier planning systems to verify their ability to meet JIT delivery requirements.
Establish escalation paths for supplier performance issues, including root cause analysis and corrective action timelines.
Integrate supplier capacity data into S&OP processes to identify potential bottlenecks during peak demand.
Define data exchange formats (e.g., EDI, XML) for purchase order acknowledgments and advance shipping notices.
Manage supplier onboarding timelines to ensure new vendors are integrated before ramp-up of new product lines.

Module 6: Production Scheduling and Capacity Planning

Sequence production runs on shared lines to minimize changeover time while meeting customer delivery commitments.
Reconcile finite capacity scheduling outputs with ERP system constraints that assume infinite capacity.
Model overtime and subcontracting costs in capacity plans to evaluate trade-offs during demand surges.
Integrate preventive maintenance schedules into production planning to avoid unplanned downtime.
Adjust master production schedules in response to raw material shortages or quality holds.
Validate scheduling logic against actual shop floor performance data to identify modeling inaccuracies.
Coordinate shift patterns across facilities to balance labor cost and throughput requirements.
Implement what-if scenario planning for new product introductions that require retooling or line modifications.

Module 7: Logistics Network Design and Execution

Evaluate trade-offs between centralized distribution and regional fulfillment centers based on delivery speed and cost.
Optimize route planning for last-mile delivery considering traffic patterns, fuel costs, and time-window constraints.
Implement dynamic load consolidation rules that balance full truckload efficiency with delivery urgency.
Integrate carrier performance data into tendering decisions, penalizing late deliveries in selection algorithms.
Design reverse logistics processes for returns, including inspection, restocking, and disposition decisions.
Configure warehouse slotting algorithms to reduce travel time based on item velocity and picking frequency.
Manage cross-dock operations by synchronizing inbound and outbound shipments within tight time windows.
Deploy yard management systems to reduce trailer dwell time and improve dock scheduling efficiency.

Module 8: Performance Monitoring and Continuous Improvement

Design control towers with drill-down capability from aggregate KPIs to transaction-level root causes.
Implement balanced scorecards that link supply chain metrics to financial outcomes like cash-to-cash cycle time.
Conduct root cause analysis for service failures using structured methods like 5 Whys or fishbone diagrams.
Standardize process documentation to support audit readiness and onboarding of new team members.
Track process improvement ROI by comparing pre- and post-implementation cycle times and error rates.
Integrate voice-of-customer feedback into supply chain design, especially for delivery reliability and packaging quality.
Schedule regular process health checks to identify degradation in automated workflows or data quality.
Facilitate cross-functional improvement workshops using Lean or Six Sigma methodologies with measurable outcomes.

Module 9: Technology Selection and Change Management

Evaluate whether to customize or configure off-the-shelf supply chain planning software based on total cost of ownership.
Plan data migration from legacy systems, including validation of historical transaction accuracy post-cutover.
Design user role hierarchies in new systems to enforce segregation of duties in procurement and inventory adjustments.
Develop training simulations using real data to prepare planners for new forecasting or S&OP tools.
Manage resistance from superusers reliant on spreadsheets by demonstrating time savings in new workflows.
Define integration patterns between cloud-based TMS and on-premise ERP using middleware or APIs.
Establish governance for sandbox environments where planners test scenarios without impacting live data.
Monitor system adoption metrics such as login frequency and feature usage to identify training gaps.