Description

This curriculum spans the design and operationalization of data-driven supply chain systems with a scope and technical specificity comparable to a multi-phase internal capability program, addressing challenges from data integration and forecasting to governance and scaling, as encountered in large-scale logistics transformations.

Module 1: Defining Data-Driven Supply Chain Objectives and KPIs

Selecting lead-time reduction versus inventory turnover as a primary KPI based on industry vertical and margin structure
Aligning forecasting accuracy targets with service level agreements across procurement, warehousing, and logistics teams
Deciding between centralized and decentralized KPI ownership in multi-echelon supply networks
Implementing lagging versus leading indicators for supplier performance monitoring
Calibrating service level metrics to account for seasonal demand volatility in retail supply chains
Integrating financial KPIs such as cash-to-cash cycle time into operational dashboards
Negotiating data-sharing SLAs with third-party logistics providers to support KPI tracking
Adjusting replenishment KPIs dynamically based on supply disruption alerts from external data feeds

Module 2: Data Integration Across Disparate Supply Chain Systems

Mapping legacy ERP item master data to modern warehouse management system identifiers without disrupting order fulfillment
Resolving unit-of-measure inconsistencies between procurement and logistics databases during integration
Designing ETL pipelines that handle partial data availability from suppliers using EDI, API, and manual uploads
Implementing change data capture for real-time inventory updates across geographically distributed DCs
Handling time zone and calendar discrepancies in global supply chain event timestamps
Establishing data ownership protocols when multiple departments contribute to a shared logistics data lake
Configuring middleware to normalize supplier shipment status codes across carriers
Validating data lineage from IoT sensors on shipping containers through to enterprise analytics platforms

Module 3: Demand Forecasting with Hybrid Data Models

Choosing between exponential smoothing and LSTM models based on SKU-level demand history length and volatility
Integrating point-of-sale data from retail partners into baseline statistical forecasts
Adjusting forecast models for known future events such as trade promotions or plant shutdowns
Managing forecast override workflows that balance planner intuition with model output
Quantifying the impact of weather data on regional demand for temperature-sensitive products
Handling intermittent demand for slow-moving SKUs using Croston’s method in production systems
Implementing forecast consumption logic to reconcile sales orders with original projections
Validating model performance using out-of-sample testing across multiple demand regimes

Module 4: Inventory Optimization Under Uncertainty

Setting safety stock levels using probabilistic models that incorporate supplier lead time variability
Allocating constrained inventory across regions during supply shortages using service-level tiering
Implementing dynamic ABC classification based on real-time demand and margin shifts
Adjusting reorder points in multi-warehouse networks to reflect cross-dock capabilities
Modeling the cost of stockouts using historical lost sales data and customer churn rates
Integrating supplier risk scores into inventory positioning decisions for dual-sourced components
Optimizing buffer stock for regulatory compliance items with long approval cycles
Rebalancing inventory targets after M&A activity disrupts existing network assumptions

Module 5: Supplier Risk Analytics and Performance Monitoring

Aggregating financial health scores, geopolitical risk indices, and on-time delivery rates into a composite supplier risk metric
Automating alerts for supplier concentration risk when a single vendor exceeds 30% of category spend
Validating supplier self-reported ESG data against third-party audit findings and news sentiment
Linking supplier quality defect rates to production downtime costs in discrete manufacturing
Implementing early warning systems using supplier payment behavior and logistics delays
Designing corrective action workflows triggered by sustained performance degradation
Quantifying the impact of currency fluctuations on supplier pricing stability
Mapping sub-tier supplier dependencies using network graph analysis for critical components

Module 6: Logistics Network Design and Simulation

Evaluating trade-offs between centralized distribution and regional fulfillment centers using total cost modeling
Simulating the impact of port congestion on inbound lead times using historical vessel tracking data
Optimizing warehouse location placement using geospatial analysis of customer density and freight lanes
Modeling carbon emissions under different transportation mode mixes for sustainability reporting
Assessing the cost-benefit of nearshoring using total landed cost simulations
Running disruption scenarios such as customs delays or natural disasters in network models
Validating network simulation outputs against actual freight invoice data
Integrating labor availability and wage rates into DC operating cost projections

Module 7: Real-Time Operational Decision Support

Deploying edge computing devices to enable real-time rerouting decisions during carrier disruptions
Implementing rule-based exception management for purchase order deviations in ERP systems
Configuring dynamic order promising logic using ATP (Available-to-Promise) with constrained resources
Integrating real-time traffic data into last-mile delivery scheduling algorithms
Triggering expedited shipping decisions based on customer order priority and inventory position
Automating transshipment recommendations between warehouses during localized stockouts
Using RFID data to validate put-away accuracy and reduce picking errors in high-volume DCs
Monitoring production line feed rates against material delivery schedules in just-in-time environments

Module 8: Governance, Ethics, and Auditability in Supply Chain AI

Documenting model assumptions and data sources for external audit of forecasting systems
Implementing access controls to prevent unauthorized manipulation of demand planning algorithms
Establishing version control for inventory optimization models deployed across business units
Conducting bias assessments on supplier scoring models for geographic or size-based discrimination
Logging all forecast override actions with justification for compliance and traceability
Designing data retention policies for shipment tracking information in line with GDPR and CCPA
Requiring impact assessments before deploying AI-driven pricing adjustments to suppliers
Auditing algorithmic decisions that result in supplier deselection or volume reallocation

Module 9: Scaling and Sustaining Data-Driven Initiatives

Refactoring prototype forecasting models into scalable microservices for enterprise deployment
Establishing cross-functional data stewardship councils to maintain data quality standards
Measuring ROI of analytics initiatives using before-and-after operational cost comparisons
Designing training programs for planners transitioning from spreadsheet-based to system-driven workflows
Implementing feedback loops from execution outcomes to refine demand sensing models
Managing technical debt in supply chain data pipelines as new data sources are onboarded
Standardizing data definitions across M&A integrations to maintain decision consistency
Planning for model drift detection and retraining schedules based on data volatility thresholds