Introduction

Data-driven decision-making is transforming logistics and supply chain management, enabling businesses to optimize operations, reduce costs, and improve efficiency. Supply chain analytics helps companies make real-time, data-driven decisions using AI, machine learning, predictive modeling, and digital dashboards.

This course provides professionals with advanced analytical skills and tools to extract insights from logistics and supply chain data. Participants will explore key performance indicators (KPIs), predictive analytics, risk management, and optimization strategies to drive efficiency and innovation in supply chain operations.

Course Objectives

By the end of this course, participants will:

• Understand the role of data analytics in logistics and supply chain management.
• Learn how to measure performance using key supply chain analytics frameworks.
• Explore AI, machine learning, and big data applications in supply chain analytics.
• Develop strategies for demand forecasting, inventory optimization, and logistics efficiency.
• Assess risk management, sustainability, and digital transformation in analytics-driven SCM.
• Gain insights into future trends in real-time analytics and AI-powered decision-making.

Who Should Attend?

This course is ideal for professionals involved in logistics, supply chain optimization, and data analytics, including:

• Supply Chain Analysts & Logistics Managers
• Data Analysts & Business Intelligence Professionals
• Procurement & Inventory Optimization Specialists
• Operations & Strategy Executives
• IT & Digital Transformation Experts in SCM
• Entrepreneurs & Business Owners Managing Logistics Networks

Course Outline

Day 1: Introduction to Supply Chain Analytics & Key Metrics

Unit 1: Understanding Supply Chain Analytics

• The Importance of Analytics in Modern Supply Chain Decision-Making
• Types of Analytics: Descriptive, Diagnostic, Predictive & Prescriptive
• Key Challenges in Implementing Data-Driven Logistics & Supply Chain Solutions
• Case Study: How Amazon Uses Big Data & AI to Optimize Its Supply Chain

Unit 2: Key Performance Indicators (KPIs) in Supply Chain Analytics

• Measuring Performance: OTIF (On-Time, In-Full), Lead Times, Cost-to-Serve
• Inventory Performance Metrics: Stock Turnover, Fill Rate, Carrying Costs
• Transportation & Logistics KPIs: Freight Costs, Route Efficiency, CO2 Emissions
• Workshop: Setting Up a KPI Dashboard for a Logistics Operation

Day 2: Data-Driven Demand Planning & Forecasting Analytics

Unit 3: Demand Forecasting & Predictive Analytics

• The Role of AI & Machine Learning in Demand Planning
• Data Sources for Forecasting: Historical Sales, Market Trends, IoT, Consumer Behavior
• Predictive Analytics Models: Regression, Time-Series Analysis, AI-Driven Demand Sensing
• Case Study: How Walmart Uses AI for Demand Prediction

Unit 4: Inventory & Warehouse Analytics

• Optimizing Stock Levels Using ABC Analysis & Inventory Turnover Ratios
• AI & IoT for Real-Time Inventory Tracking & Warehouse Efficiency
• Inventory Cost Optimization: Reducing Stockouts & Overstocking Risks
• Workshop: Building an Inventory Optimization Model

Day 3: Logistics & Transportation Analytics

Unit 5: Data-Driven Transportation & Route Optimization

• AI-Powered Route Planning for Faster Deliveries & Cost Reduction
• Real-Time GPS & IoT Tracking for Fleet Performance Monitoring
• AI & Predictive Maintenance for Vehicle Uptime Optimization
• Case Study: How DHL Uses AI & IoT for Smart Logistics Operations

Unit 6: Freight Cost Analysis & Logistics Network Optimization

• Understanding Total Landed Cost (TLC) & Freight Cost Benchmarking
• AI for Dynamic Freight Pricing & Supplier Rate Negotiation
• Digital Freight Marketplaces & Cloud-Based Logistics Platforms
• Workshop: Conducting a Freight Cost Reduction Analysis

Day 4: Risk Management & Supply Chain Resilience Analytics

Unit 7: Identifying & Managing Supply Chain Risks with Analytics

• AI for Risk Prediction & Scenario Planning in Supply Chains
• Real-Time Disruption Detection: Geopolitical, Climate, Economic Factors
• Digital Twin Technology for Simulating & Preventing Supply Chain Failures
• Case Study: How Tesla Uses Risk Analytics to Mitigate Supply Chain Disruptions

Unit 8: Sustainability & ESG Analytics in Supply Chain Operations

• Carbon Footprint Tracking & CO2 Reduction Strategies
• Sustainable Supply Chain KPIs: Energy Usage, Green Logistics, Reverse Logistics
• AI & Blockchain for ESG Compliance & Ethical Sourcing Transparency
• Workshop: Developing a Sustainability-Focused Supply Chain Dashboard

Day 5: Future Trends & Strategic Implementation of Supply Chain Analytics

Unit 9: AI, Blockchain & IoT in Supply Chain Analytics

• The Future of Autonomous Decision-Making with AI & Digital Twins
• Blockchain for Data Integrity & Supply Chain Transparency
• IoT & Smart Sensors for Real-Time Performance Monitoring
• Panel Discussion: Experts on the Future of Data-Driven Supply Chains

Unit 10: Implementing a Data-Driven Supply Chain Strategy

• Building a Supply Chain Analytics Framework for Long-Term Success
• Measuring ROI: Cost Reduction, Service Levels & Predictive Accuracy
• Final Project: Designing a Supply Chain Analytics Dashboard & Optimization Plan
• Course Wrap-Up & Certification

Training Methodology

• Interactive Lectures & Real-World Case Studies from Industry Leaders
• Hands-On Workshops with Predictive Analytics, AI & Data Visualization Tools
• Live Demonstrations of IoT, Blockchain & Digital Logistics Platforms
• Simulations & Data-Driven Decision-Making Exercises for Supply Chain Optimization
• Expert Panel Discussions & Group Strategy Sessions