Description

This curriculum spans the design and operationalization of data systems across a global supply chain, comparable in scope to a multi-phase advisory engagement addressing data integration, governance, and analytics deployment across procurement, logistics, and inventory functions.

Module 1: Defining Data Scope and Integration Boundaries in Supply Chain Ecosystems

Select data sources from tier-1 suppliers, logistics providers, and internal ERP systems based on lead time variability and inventory turnover impact.
Map EDI, API, and flat-file ingestion formats across heterogeneous partners, standardizing timestamps and units of measure.
Decide whether to consolidate master data (e.g., SKUs, locations) in a central registry or maintain federated ownership with reconciliation protocols.
Implement change data capture (CDC) for high-frequency updates from warehouse management systems without overloading source databases.
Assess the cost-benefit of real-time versus batch integration for supplier shipment confirmations based on service-level agreements.
Design schema evolution strategies for purchase order data when suppliers modify their data structures without notice.
Establish fallback mechanisms for data pipelines when carrier tracking APIs exceed rate limits or return inconsistent statuses.
Negotiate data-sharing SLAs with third-party logistics providers to ensure minimum update frequency and accuracy thresholds.

Module 2: Building Scalable Data Ingestion Architectures

Choose between Kafka and Pulsar for event streaming based on geo-distribution needs and message retention policies across global warehouses.
Configure backpressure handling in ingestion pipelines during peak order periods like holiday surges or promotional campaigns.
Partition shipment event data by carrier, region, and shipment ID to balance query performance and cluster load.
Implement idempotent processing logic to handle duplicate ASN (Advanced Shipping Notice) messages from suppliers.
Encrypt sensitive data (e.g., customs values, customer addresses) in transit and at rest using customer-managed keys.
Size cluster resources for batch ingestion jobs based on historical peak volumes from seasonal demand spikes.
Monitor ingestion latency from port authorities and customs brokers to detect systemic delays in data availability.
Deploy schema validation at ingestion points to reject malformed inventory snapshot files from regional DCs.

Module 3: Master Data Management and Entity Resolution

Resolve SKU discrepancies when the same product is labeled differently across procurement, warehouse, and sales systems.
Design probabilistic matching rules to link supplier entities using name, tax ID, and address when golden records are missing.
Implement a reconciliation workflow for location hierarchies when warehouse codes differ between TMS and WMS platforms.
Decide whether to use a hub-and-spoke or registry-based MDM architecture based on organizational data governance maturity.
Track lineage of master data changes to audit corrections made during supplier onboarding or merger integrations.
Automate conflict resolution for part numbers that vary between engineering BOMs and procurement catalogs.
Enforce data stewardship roles for supplier master updates to prevent unauthorized changes affecting procurement contracts.
Integrate MDM with data quality tools to flag incomplete or inconsistent records before they propagate to analytics layers.

Module 4: Real-Time Visibility and Event Processing

Define event schemas for shipment milestones (e.g., gate-in, customs clearance) that align with internal SLAs and customer commitments.
Configure complex event processing (CEP) rules to detect multi-leg delays in intermodal freight movements.
Balance event granularity—tracking containers vs. pallets vs. individual items—based on tracking technology and use case ROI.
Integrate GPS and IoT sensor data from reefer containers into streaming pipelines for temperature deviation alerts.
Set up deduplication logic for event bursts when telematics devices reconnect after network outages.
Route high-priority exception events (e.g., port congestion, customs hold) to operational dashboards and alert systems.
Store event state in durable key-value stores to support long-running shipment tracking workflows across days or weeks.
Optimize event time watermarking to handle late-arriving data from regions with poor connectivity.

Module 5: Demand Sensing and Forecasting with Big Data

Incorporate point-of-sale data, social sentiment, and weather feeds into short-term demand models for perishable goods.
Compare performance of traditional time-series models versus deep learning (e.g., LSTMs) on sparse, intermittent demand data.
Manage cold-start forecasting for new SKUs using analogous product hierarchies and launch campaign data.
Adjust forecast models dynamically when promotional data from marketing platforms arrives late or in inconsistent formats.
Implement backtesting frameworks to evaluate forecast accuracy across regions with differing seasonality patterns.
Handle structural breaks in demand due to external shocks (e.g., pandemics, trade restrictions) using changepoint detection.
Allocate compute resources for daily forecast runs based on SKU criticality and volume thresholds.
Version control forecasting models and input data to reproduce results during audit or dispute resolution.

Module 6: Inventory Optimization and Network Analytics

Model safety stock levels across multi-echelon networks using lead time distributions from supplier performance data.
Integrate transportation cost matrices from freight audits into inventory placement simulations.
Quantify the trade-off between centralization and regional stocking based on service level targets and holding costs.
Simulate stockout cascades in distribution networks when a key DC experiences operational disruption.
Apply clustering techniques to group SKUs by demand variability and lifecycle stage for tailored stocking policies.
Update replenishment parameters automatically when supplier lead time variance exceeds predefined thresholds.
Validate inventory accuracy by reconciling WMS cycle counts with RFID or barcode scan data streams.
Monitor obsolescence risk by tracking shelf age against expiration dates in cold chain logistics.

Module 7: Data Governance and Compliance in Global Supply Chains

Classify data elements (e.g., origin country, HTS codes) for GDPR, CCPA, and customs compliance based on jurisdiction.
Implement data retention policies for shipment records in line with international trade regulation requirements.
Audit access logs to sensitive supplier cost data to detect unauthorized queries or exports.
Mask or tokenize supplier bank account and tax ID information in non-production environments.
Document data lineage for export-controlled components to support regulatory audits.
Enforce role-based access controls for inventory data based on organizational hierarchy and need-to-know principles.
Establish data ownership models for shared supply chain platforms involving multiple legal entities.
Validate data accuracy claims from suppliers using third-party verification services or blockchain-based attestations.

Module 8: Performance Monitoring and Anomaly Detection

Define KPIs for on-time in-full (OTIF) delivery using timestamped events from loading, transit, and receipt systems.
Build statistical baselines for carrier performance metrics and trigger alerts for deviations beyond control limits.
Apply unsupervised learning to detect anomalous patterns in fuel surcharge billing across carriers.
Correlate supplier defect rates with inbound inspection data and production line stoppages.
Monitor data pipeline health using synthetic transactions that simulate end-to-end shipment processing.
Diagnose root causes of forecast bias by analyzing residuals across product categories and demand planners.
Track data freshness SLAs for critical feeds (e.g., port departure status) and escalate delays automatically.
Visualize supply chain risk exposure using network graphs that highlight single-source dependencies and congestion points.

Module 9: Cross-Functional Data Product Deployment

Package inventory optimization models as REST APIs for consumption by procurement and logistics planning tools.
Design data contracts between analytics teams and application developers to ensure backward compatibility.
Deploy supplier risk scores into procurement portals with refresh intervals aligned to sourcing cycle frequency.
Integrate predictive lead time estimates into order promising systems for customer-facing commitments.
Manage versioning of data products when underlying data sources undergo structural changes.
Instrument data product usage to identify underutilized models and deprecate low-impact services.
Coordinate release schedules for data products with ERP and TMS upgrade windows to minimize integration conflicts.
Establish feedback loops from planners to data science teams to refine assumptions in inventory simulation models.