Introduction

In today’s fast-paced, data-driven world, Big Data Analytics is transforming Supply Chain Management (SCM) by providing actionable insights, optimizing operations, and enhancing decision-making. Companies leveraging big data, AI, and predictive analytics gain a competitive edge by improving demand forecasting, reducing costs, enhancing visibility, and mitigating risks. This course provides supply chain professionals with the skills to analyze vast amounts of data, extract meaningful insights, and drive data-informed decision-making for efficient, resilient, and future-ready supply chains.

Course Objectives

By the end of this course, participants will be able to:

• Understand Big Data Analytics and its role in modern supply chains.
• Utilize predictive and prescriptive analytics for better forecasting.
• Leverage machine learning and AI for supply chain optimization.
• Improve inventory management and warehouse efficiency with data analytics.
• Apply real-time data for logistics, transportation, and fleet optimization.
• Use big data to enhance supplier collaboration and risk management.
• Integrate big data with IoT, blockchain, and cloud computing.

Who Should Attend?

This course is designed for:

• Supply Chain Managers & Directors looking to optimize decision-making.
• Logistics & Operations Leaders enhancing efficiency with data insights.
• IT & Digital Transformation Professionals implementing analytics in SCM.
• Procurement & Sourcing Managers using data-driven vendor evaluation.
• Data Scientists & Analysts working on supply chain applications.
• Retail & E-commerce Professionals leveraging big data for demand forecasting.

Course Outline

Day 1: Introduction to Big Data in Supply Chain Management

Morning Session: Understanding Big Data & Its Role in SCM

• What is Big Data? Key Characteristics (Volume, Variety, Velocity, Veracity, and Value).
• The evolution of data-driven supply chain management.
• Big data vs. traditional supply chain decision-making.
• Case study: How Amazon, Walmart, and DHL use big data in supply chain operations.

Afternoon Session: Data Collection & Processing in Supply Chain

• Sources of big data: IoT devices, RFID, social media, ERP, and CRM systems.
• Data storage and processing: Cloud computing, data lakes, and warehouses.
• Workshop: Building a data collection framework for supply chains.

Day 2: Predictive Analytics & Forecasting in Supply Chain

Morning Session: Demand Forecasting with Big Data

• Using historical data and AI-driven models for demand prediction.
• Improving inventory planning and avoiding stockouts/overstocks.
• Case study: How Zara optimizes demand forecasting with real-time data.

Afternoon Session: Supply Chain Risk Analytics & Disruption Prediction

• Using data analytics for risk identification and mitigation.
• AI-powered predictive models for supplier reliability assessment.
• Workshop: Creating a risk analytics dashboard for supply chains.

Day 3: Big Data for Logistics, Transportation & Warehouse Optimization

Morning Session: Data-Driven Logistics & Route Optimization

• Real-time analytics for fleet management and delivery efficiency.
• Big data applications in route planning, fuel efficiency, and traffic prediction.
• Case study: UPS’s ORION system for big data-powered logistics.

Afternoon Session: Warehouse Analytics & Smart Inventory Management

• How big data enhances warehouse operations and reduces waste.
• AI-driven automated replenishment and storage optimization.
• Workshop: Developing a data-driven warehouse management strategy.

Day 4: Supplier Relationship & Performance Analytics

Morning Session: Supplier Performance & Procurement Analytics

• Using big data for supplier evaluation and selection.
• Enhancing supplier collaboration and reducing procurement risks.
• Case study: How Apple and Toyota leverage big data for supplier management.

Afternoon Session: Sustainability & Compliance Through Data Analytics

• How big data ensures ESG (Environmental, Social, Governance) compliance.
• Monitoring carbon footprint, ethical sourcing, and sustainability with data.
• Group discussion: The future of sustainable supply chains with big data.

Day 5: Future Trends, AI & Big Data Integration in SCM

Morning Session: AI, Machine Learning & Blockchain for Data-Driven SCM

• How AI and machine learning enhance supply chain analytics.
• The role of blockchain in secure and transparent data transactions.
• Case study: DHL’s AI-powered predictive analytics for logistics.

Afternoon Session: Building a Big Data Strategy for Supply Chain

• Overcoming challenges in big data adoption (cost, complexity, security issues).
• Developing a data-driven decision-making culture in supply chain teams.
• Capstone project: Designing a big data analytics roadmap for an SCM organization.

Training Methodology

This course follows a hands-on, interactive, and real-world application approach, including:

• Live demonstrations of big data analytics tools (Power BI, Tableau, Python, SQL, R, AI-driven analytics platforms).
• Case studies from top companies (Amazon, Walmart, UPS, DHL, Zara, Apple, Toyota).
• Workshops on predictive analytics, risk assessment, and warehouse optimization.
• Group discussions on ethical considerations and data-driven sustainability.
• Capstone project: Developing a big data strategy for real-world supply chain applications.

Certification

Upon successful completion, participants will receive:

• A Certificate of Completion in Big Data Analytics in Supply Chain Management from Learnify® Training.
• A Digital Badge for LinkedIn and professional recognition.

This certification validates expertise in big data-driven supply chain optimization, predictive analytics, and AI-powered decision-making, preparing professionals for future-ready supply chain leadership roles.