Introduction

In an increasingly complex global market, organizations must harness the power of data to make informed supply chain decisions. Advanced Supply Chain Analytics integrates big data, AI, and predictive modeling to optimize logistics, reduce costs, and enhance operational efficiency.

This course provides professionals with cutting-edge analytical tools and techniques to gain actionable insights, improve forecasting accuracy, and mitigate supply chain risks. By the end of this program, participants will be equipped with the knowledge to drive data-driven supply chain transformations, ensuring competitiveness in the digital era.

Course Objectives

By the end of this course, participants will:

• Understand key principles and methodologies in supply chain analytics.
• Learn how to collect, clean, and analyze supply chain data for better decision-making.
• Use predictive analytics, AI, and machine learning for demand forecasting.
• Optimize inventory, logistics, and transportation through data-driven strategies.
• Enhance risk management and resilience in supply chains using analytical tools.
• Apply real-world case studies and simulations to solve supply chain challenges.

Who Should Attend?

This training program is designed for professionals involved in supply chain optimization, logistics, procurement, and financial operations, including:

• Supply Chain & Logistics Managers
• Data Analysts & Business Intelligence Professionals
• Operations & Procurement Specialists
• Finance & Accounting Experts
• IT & AI Professionals in Supply Chain Analytics
• Business Owners & Entrepreneurs

Course Outline

Day 1: Introduction to Supply Chain Analytics & Data Fundamentals

Unit 1: The Role of Analytics in Modern Supply Chains

• What is Supply Chain Analytics?
• Evolution of Data-Driven Supply Chain Management
• Types of Supply Chain Analytics: Descriptive, Predictive, and Prescriptive
• Case Study: How Top Companies Use Supply Chain Analytics

Unit 2: Data Collection, Processing, and Visualization

• Data Sources in Supply Chains: IoT, ERP, CRM, and External Market Data
• Data Cleaning & Preprocessing Techniques
• Data Visualization Best Practices with Power BI/Tableau
• Workshop: Hands-on Data Cleaning & Visualization Exercise

Day 2: Predictive Analytics & Demand Forecasting

Unit 3: Statistical & AI-Based Forecasting Methods

• Introduction to Predictive Analytics in Supply Chains
• Machine Learning Algorithms for Demand Forecasting
• AI-Powered Predictive Models for Supply Chain Optimization
• Workshop: Building a Demand Forecasting Model

Unit 4: Inventory Optimization Through Data Analytics

• Just-in-Time (JIT) & Economic Order Quantity (EOQ) Models
• Multi-Echelon Inventory Optimization
• Reducing Stockouts & Overstocks Using Predictive Analytics
• Case Study: How Amazon & Walmart Optimize Inventory with AI

Day 3: Supply Chain Optimization & Network Design

Unit 5: Logistics & Transportation Optimization

• Data-Driven Route Planning & Fleet Management
• AI & IoT in Logistics Tracking & Monitoring
• Cost Reduction Strategies in Transportation Analytics
• Simulation: Optimizing a Real-World Logistics Network

Unit 6: Supply Chain Network Design & Optimization

• Advanced Network Modeling Techniques
• Dynamic Sourcing & Supplier Performance Analytics
• Distribution Center Location Selection Using Data Analytics
• Workshop: Designing an Optimal Supply Chain Network

Day 4: Risk Management & Real-Time Supply Chain Intelligence

Unit 7: Risk Identification & Mitigation Through Analytics

• Identifying Supply Chain Risks Using Big Data
• Scenario Analysis & Stress Testing Supply Chains
• AI & Blockchain for Supply Chain Security & Fraud Prevention
• Group Activity: Developing a Risk-Resilient Supply Chain Strategy

Unit 8: Real-Time Supply Chain Visibility & Automation

• IoT & Cloud-Based Analytics for Real-Time Monitoring
• Digital Twin Technology in Supply Chain Analytics
• AI-Driven Automated Decision Making in Supply Chains
• Case Study: How Leading Companies Use Real-Time Data for Supply Chain Resilience

Day 5: Future Trends & Strategic Implementation of Analytics

Unit 9: Emerging Trends in Supply Chain Analytics

• The Future of AI & Big Data in Supply Chains
• Predictive Maintenance & Smart Manufacturing
• The Impact of Quantum Computing on Supply Chain Analytics
• Panel Discussion: Industry Experts on the Future of Supply Chain Analytics

Unit 10: Strategic Roadmap for Implementing Advanced Analytics

• Developing a Data-Driven Supply Chain Strategy
• Overcoming Barriers to Digital Transformation in Supply Chains
• Final Project: Designing a Smart Supply Chain Analytics Model
• Course Wrap-Up & Certification

Training Methodology

• Interactive Lectures & Case Studies
• Hands-On Workshops with Real-World Data
• Predictive Analytics & AI-Based Simulations
• Live Demonstrations Using Data Tools (Python, Power BI, Tableau, Excel)
• Group Strategy Sessions & Panel Discussions