Description

This curriculum spans the design and operationalization of data systems across a global supply chain, comparable in scope to a multi-phase digital transformation initiative involving data architecture, real-time analytics, and AI governance across procurement, logistics, and compliance functions.

Module 1: Defining Big Data Requirements in Supply Chain Contexts

Select data ingestion frequency (real-time vs. batch) based on supplier lead time variability and inventory turnover rates.
Determine which supply chain nodes (e.g., ports, warehouses, last-mile carriers) require sensor-level telemetry integration.
Negotiate data-sharing SLAs with third-party logistics providers to ensure consistency in format and timeliness.
Classify data sources by criticality (e.g., customs clearance feeds vs. internal warehouse logs) for prioritized processing.
Decide on data retention policies for shipment records in compliance with international trade regulations.
Map legacy EDI systems to modern data pipelines, identifying transformation rules for ASN and PO documents.
Assess whether to build a centralized data lake or maintain federated data marts per region.
Define master data ownership for SKUs, suppliers, and locations across global subsidiaries.

Module 2: Data Architecture for Multi-Tier Supply Networks

Design schema for supplier-tier hierarchies that support recursive querying for sub-tier risk exposure.
Implement change data capture (CDC) for supplier databases to track contract and capacity updates.
Structure time-series storage for IoT data from shipping containers (temperature, humidity, GPS).
Choose between graph databases and relational models for supplier dependency mapping.
Partition historical shipment data by trade lane and customs zone to optimize query performance.
Integrate B2B APIs from freight forwarders into the data fabric using standardized adapters.
Isolate sensitive data (e.g., dual-use goods) using column-level encryption and access zoning.
Model probabilistic lead times using stochastic data structures instead of deterministic fields.

Module 3: Real-Time Visibility and Event Processing

Configure stream processing windows to detect shipment delays using vessel AIS and port congestion data.
Set thresholds for event alerts (e.g., temperature breach) that balance sensitivity and operator fatigue.
Deploy edge computing nodes on transport vehicles to preprocess sensor data before transmission.
Orchestrate event-driven workflows that trigger re-routing when customs hold events occur.
Normalize event schemas from disparate carriers to enable cross-vendor anomaly detection.
Implement deduplication logic for GPS pings received from redundant tracking devices.
Design fallback mechanisms for event processing during API outages from tracking providers.
Log and audit all automated event responses for compliance with audit requirements.

Module 4: Predictive Analytics for Demand and Supply Planning

Select forecasting models (e.g., Prophet, ARIMAX) based on product lifecycle stage and data availability.
Incorporate external signals (weather, port strikes) as exogenous variables in demand models.
Backtest forecast accuracy across different product categories and geographic zones.
Adjust safety stock calculations dynamically using predicted lead time variance.
Balance model complexity against retraining frequency and operational interpretability.
Validate supplier capacity forecasts against historical fulfillment rates and order volatility.
Implement guardrails to prevent automated replenishment when model confidence falls below threshold.
Version control model inputs and parameters to support audit and rollback capabilities.

Module 5: Risk Management and Resilience Modeling

Quantify supplier risk scores using financial health data, geopolitical indices, and delivery performance.
Simulate cascading disruptions using network models that include sub-tier dependencies.
Integrate real-time news and social media feeds into risk dashboards with NLP filtering.
Define escalation paths for risk events based on financial exposure and substitution lead time.
Model inventory positioning strategies (e.g., safety stock vs. dual sourcing) under multiple scenarios.
Validate resilience models using historical disruption data from past supply chain events.
Establish thresholds for automatic rerouting or expediting based on predicted delay impact.
Document assumptions in risk models for executive review and insurance underwriting.

Module 6: Optimization of Logistics and Inventory

Formulate mixed-integer programs for multi-echelon inventory placement across DCs and regional hubs.
Integrate carbon cost as a variable in transportation mode selection algorithms.
Adjust optimization constraints dynamically based on real-time fuel prices and carrier capacity.
Balance service level targets against inventory carrying costs in network design models.
Validate route optimization outputs against actual driver behavior and local regulations.
Implement rolling horizon planning to reconcile tactical models with operational execution.
Monitor solver performance and adjust problem decomposition for large-scale networks.
Log optimization decisions and inputs to support post-hoc analysis of cost deviations.

Module 7: Data Governance and Compliance in Global Operations

Map data flows across jurisdictions to comply with GDPR, CCPA, and local data sovereignty laws.
Implement data lineage tracking to support audit requests for customs and trade compliance.
Define role-based access controls for procurement, logistics, and finance teams.
Standardize data quality metrics (completeness, timeliness) across global supply chain units.
Establish data stewardship roles for master data entities in a matrix organizational structure.
Document data provenance for ESG reporting on carbon emissions and ethical sourcing.
Conduct regular data privacy impact assessments for new tracking technologies.
Enforce data masking for sensitive supplier contract terms in non-authorized views.

Module 8: Integration with ERP and Procurement Systems

Design bi-directional sync between analytics platforms and ERP for purchase order status updates.
Handle version mismatches when integrating with legacy SAP ECC versus S/4HANA instances.
Map analytics-generated alerts to procurement workflow tasks in Coupa or Ariba.
Implement reconciliation jobs to resolve discrepancies between forecasted and actual PO volumes.
Orchestrate data validation checks before pushing recommended orders into ERP.
Configure middleware to manage API rate limits from cloud procurement platforms.
Preserve audit trails when automated systems update supplier performance ratings.
Align data refresh cycles between batch analytics runs and ERP close periods.

Module 9: Scaling and Operating AI Systems in Production

Design model monitoring dashboards that track prediction drift in lead time forecasts.
Implement canary deployments for new routing algorithms in low-risk trade lanes first.
Allocate compute resources for batch jobs during off-peak hours to avoid ERP contention.
Establish incident response protocols for data pipeline failures affecting planning cycles.
Containerize analytics workloads to ensure consistency across development and production.
Define SLAs for data pipeline latency and model inference response times.
Automate rollback procedures when model performance degrades beyond acceptable thresholds.
Conduct capacity planning for data growth based on projected shipment volume increases.