Course Introduction

Supply chains generate large and continuously changing volumes of data across procurement, suppliers, inventory, warehouses, transportation, orders, shipments, customers, and logistics operations. The Supply Chain and Logistics Data Engineering Training Course provides a comprehensive foundation for designing reliable data architectures, pipelines, integration workflows, and analytical platforms that transform fragmented supply chain information into trusted and actionable data assets.

Modern supply chain organizations depend on timely and accurate information to understand inventory positions, monitor shipments, evaluate supplier performance, optimize warehouse operations, forecast demand, and coordinate transportation activities. This course develops the technical capabilities required to collect, integrate, cleanse, transform, store, govern, and monitor data generated throughout complex supply chain and logistics ecosystems.

Participants will explore the complete supply chain data engineering lifecycle, beginning with operational data sources such as enterprise resource planning systems, warehouse management systems, transportation management systems, procurement platforms, order management applications, telematics devices, and supplier portals. The course explains how these heterogeneous sources can be connected through scalable batch and streaming architectures to support operational and analytical workloads.

The curriculum addresses the specialized characteristics of logistics data, including orders, inventory movements, shipment milestones, purchase orders, delivery events, warehouse transactions, transportation routes, carrier information, supplier records, product hierarchies, locations, and customer demand. Participants will learn engineering approaches for resolving inconsistent identifiers, incomplete records, duplicate transactions, delayed events, changing schemas, and other challenges that can affect supply chain visibility and analytical accuracy.

Advanced topics cover data lakes, warehouses, lakehouse architectures, event-driven pipelines, real-time shipment tracking, master data management, data quality, lineage, governance, orchestration, observability, and cloud-based data platforms. Participants will also examine how engineered data products can support demand forecasting, inventory optimization, route analytics, supplier performance management, logistics cost analysis, warehouse intelligence, and predictive operational use cases.

By the end of the Supply Chain and Logistics Data Engineering Training Course, participants will be able to design and evaluate scalable data engineering solutions that connect supply chain applications, operational systems, external partners, and analytical platforms. The course combines architectural principles, practical pipeline engineering, integration techniques, governance practices, and operational controls required to create dependable data foundations for digitally enabled supply chains.

Duration

10 days

Who Should Attend

• Supply chain data engineers responsible for building and maintaining enterprise data pipelines and analytical platforms.

• Logistics professionals working with transportation, shipment, warehouse, delivery, and operational datasets.

• Data architects designing scalable data platforms for supply chain visibility and logistics intelligence.

• ETL developers integrating ERP, WMS, TMS, procurement, order management, and supplier systems.

• Cloud engineers developing modern data lake, warehouse, and lakehouse environments for supply chain organizations.

• Database administrators managing high-volume transactional and analytical supply chain databases.

• Business intelligence professionals developing dashboards and analytical solutions for supply chain operations.

• Supply chain analysts working with inventory, procurement, demand, transportation, and supplier performance data.

• Logistics technology specialists integrating telematics, tracking, IoT, carrier, and warehouse data sources.

• Procurement and supplier management professionals requiring integrated and trusted supplier information.

• Operations managers seeking stronger data foundations for logistics planning and operational decision-making.

• Data governance professionals responsible for supply chain data quality, ownership, lineage, security, and standards.

• Enterprise integration specialists connecting internal systems with suppliers, carriers, marketplaces, and logistics partners.

• Project managers and technical leads implementing supply chain digitization, data modernization, and analytics initiatives.

Course Objectives

• Develop a comprehensive understanding of supply chain and logistics data architectures spanning procurement, inventory, warehousing, transportation, orders, suppliers, customers, and delivery operations.

• Design scalable data ingestion pipelines that consolidate information from ERP, WMS, TMS, procurement, order management, supplier portals, telematics, and external logistics systems.

• Apply batch and streaming data engineering techniques to process transactional records, shipment events, inventory movements, tracking updates, and operational telemetry efficiently.

• Build robust transformation workflows that standardize products, suppliers, locations, orders, shipments, inventory quantities, timestamps, status codes, and transportation information.

• Implement advanced data quality controls for identifying duplicate transactions, missing records, inconsistent identifiers, invalid quantities, delayed events, and inaccurate operational information.

• Design analytical data models that support inventory analysis, demand planning, supplier performance, transportation analytics, warehouse intelligence, order fulfillment, and logistics cost management.

• Integrate supply chain data across enterprise applications and external partners while maintaining consistent business definitions, identifiers, relationships, and data lineage.

• Engineer near-real-time data pipelines that support shipment visibility, inventory monitoring, warehouse events, delivery tracking, and operational exception management.

• Apply master data management principles to create consistent representations of products, suppliers, customers, facilities, carriers, locations, and other critical supply chain entities.

• Implement governance, security, metadata, lineage, retention, and access management practices appropriate for commercially sensitive supply chain and logistics information.

• Establish pipeline observability and operational monitoring capabilities that identify failures, delays, volume anomalies, schema changes, data-quality issues, and processing bottlenecks.

• Design resilient and scalable supply chain data platforms capable of supporting growing transaction volumes, new data sources, real-time workloads, advanced analytics, and future digital transformation initiatives.

Comprehensive Course Outline

Module 1: Foundations of Supply Chain and Logistics Data Engineering

• Understand the role of data engineering across procurement, inventory, warehousing, transportation, fulfillment, and distribution operations.

• Examine major supply chain data domains including products, suppliers, orders, shipments, locations, inventory, and customers.

• Explore the characteristics of transactional, event-based, master, reference, and time-series logistics data.

• Review the complete lifecycle from operational data generation through ingestion, transformation, storage, analytics, and reporting.

Module 2: Supply Chain Systems and Data Sources

• Examine ERP platforms and their role in generating procurement, purchasing, financial, inventory, and order-related data.

• Explore warehouse management systems and the operational information generated through receiving, picking, packing, and dispatch activities.

• Understand transportation management systems, carrier platforms, routing applications, and shipment execution datasets.

• Analyze external data sources including suppliers, marketplaces, logistics partners, telematics systems, and tracking platforms.

Module 3: Supply Chain Data Integration Architecture

• Design integration architectures connecting ERP, WMS, TMS, procurement, CRM, supplier, and logistics applications.

• Compare APIs, files, databases, event streams, message queues, and partner interfaces for supply chain data exchange.

• Develop source-to-target mappings that preserve business definitions and relationships across heterogeneous operational systems.

• Establish integration patterns that support both centralized analytical platforms and distributed operational data requirements.

Module 4: Supply Chain Data Ingestion and Pipeline Engineering

• Design reliable batch ingestion pipelines for orders, purchase orders, inventory transactions, supplier records, and shipment histories.

• Develop streaming ingestion architectures for real-time tracking events, warehouse activity, transportation updates, and operational alerts.

• Implement incremental loading, checkpointing, retry mechanisms, and recovery strategies for high-volume supply chain pipelines.

• Manage late-arriving data, source-system changes, schema evolution, backfills, and varying data delivery frequencies.

Module 5: Inventory and Warehouse Data Engineering

• Develop data models for inventory balances, stock movements, warehouse transactions, locations, bins, and product availability.

• Integrate receiving, put-away, picking, packing, dispatch, returns, and cycle-count information into analytical datasets.

• Handle inventory adjustments, reservations, transfers, stock discrepancies, and changes in warehouse operating conditions.

• Build scalable pipelines that support inventory visibility, warehouse performance analysis, and operational exception monitoring.

Module 6: Transportation and Shipment Data Engineering

• Engineer data pipelines for shipments, routes, carriers, vehicles, delivery events, freight transactions, and transportation milestones.

• Integrate GPS, telematics, carrier APIs, tracking platforms, and transportation management system data into unified datasets.

• Process shipment status changes, estimated arrival times, delivery confirmations, route events, and transportation exceptions.

• Develop analytical structures for transportation performance, route efficiency, carrier analysis, delivery reliability, and logistics cost evaluation.

Module 7: Supply Chain Data Quality and Validation

• Establish quality rules for completeness, accuracy, consistency, timeliness, uniqueness, validity, and referential integrity across supply chain datasets.

• Detect duplicate orders, inconsistent product identifiers, invalid quantities, missing shipment events, and conflicting operational records.

• Develop reconciliation processes that compare orders, shipments, inventory movements, receipts, invoices, and delivery confirmations.

• Implement automated quality monitoring, exception reporting, remediation workflows, and historical quality tracking for critical datasets.

Module 8: Supply Chain Data Lakes, Warehouses, and Lakehouses

• Design scalable data lake architectures for storing raw, standardized, curated, and historical supply chain information.

• Develop analytical warehouse models supporting procurement, inventory, transportation, order fulfillment, supplier, and logistics reporting.

• Evaluate lakehouse architectures for combining large-scale operational data with analytical and machine learning workloads.

• Apply partitioning, clustering, indexing, compression, and storage optimization techniques for high-volume supply chain datasets.

Module 9: Real-Time Supply Chain and Logistics Data Processing

• Explore event-driven architectures for shipment tracking, warehouse events, inventory changes, order updates, and delivery notifications.

• Design low-latency pipelines that provide near-real-time visibility into logistics operations and supply chain exceptions.

• Apply stream processing techniques including filtering, enrichment, aggregation, windowing, correlation, and event sequencing.

• Handle duplicate events, out-of-order messages, temporary connectivity failures, late data, and high-volume event bursts.

Module 10: Master Data and Supply Chain Entity Integration

• Establish consistent master data models for products, suppliers, customers, warehouses, facilities, carriers, and geographic locations.

• Resolve inconsistent identifiers and duplicate representations of supply chain entities across enterprise applications.

• Apply reference data management practices for product classifications, units of measure, locations, shipment statuses, and business codes.

• Build unified entity relationships that support reliable analytics across procurement, inventory, logistics, fulfillment, and customer operations.

Module 11: Demand, Orders, and Fulfillment Data Engineering

• Engineer integrated datasets combining customer orders, demand signals, sales transactions, forecasts, and fulfillment information.

• Prepare analytical data structures for demand planning, order cycle analysis, service-level monitoring, and fulfillment performance.

• Process order lifecycle events from creation and allocation through picking, shipment, delivery, cancellation, and return.

• Support demand forecasting workflows by producing consistent historical, transactional, promotional, seasonal, and operational datasets.

Module 12: Supplier, Procurement, and Cost Data Engineering

• Integrate supplier profiles, purchase orders, contracts, receipts, invoices, and procurement transactions into governed data products.

• Develop pipelines that support supplier performance, lead-time analysis, purchase-price analysis, and procurement compliance reporting.

• Reconcile purchasing, receiving, invoicing, and payment-related records to identify discrepancies and improve financial visibility.

• Build analytical datasets for evaluating procurement activity, supplier reliability, sourcing patterns, and supply continuity indicators.

Module 13: Data Governance, Security, and Lineage

• Establish governance frameworks covering ownership, stewardship, metadata, quality standards, lineage, and supply chain data lifecycle management.

• Implement access controls and security practices for commercially sensitive supplier, customer, pricing, inventory, and logistics information.

• Develop lineage capabilities that trace supply chain metrics from operational sources through transformations to analytical outputs.

• Apply retention, auditing, privacy, contractual, and regulatory considerations to enterprise supply chain information management.

Module 14: Pipeline Orchestration, Monitoring, and Observability

• Design workflow orchestration for scheduled, dependency-driven, and event-triggered supply chain data pipelines.

• Implement monitoring for processing failures, latency, throughput, data volumes, dependency problems, and integration interruptions.

• Develop alerts for unusual inventory changes, missing shipment data, schema modifications, quality failures, and abnormal processing patterns.

• Establish operational procedures for troubleshooting, reruns, backfills, disaster recovery, controlled releases, and pipeline maintenance.

Module 15: Advanced Supply Chain Analytics and Intelligent Data Products

• Prepare engineered datasets for demand forecasting, inventory optimization, transportation analytics, supplier intelligence, and fulfillment analysis.

• Develop feature engineering pipelines supporting predictive models for delivery delays, demand changes, inventory risks, and operational exceptions.

• Integrate machine learning workflows with governed supply chain data platforms while maintaining reproducible and reliable data preparation.

• Explore analytical architectures for digital supply chains, predictive logistics, warehouse intelligence, and proactive operational decision support.

Module 16: Capstone Supply Chain Data Engineering Project

• Design an end-to-end architecture integrating procurement, inventory, warehouse, transportation, order, supplier, and shipment data sources.

• Build a representative pipeline incorporating ingestion, transformation, validation, storage, orchestration, monitoring, and analytical data preparation.

• Develop a unified analytical dataset supporting supply chain visibility, inventory intelligence, logistics performance, or fulfillment analysis.

• Present the completed solution with architecture documentation, data lineage, quality controls, scalability considerations, and operational procedures.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.