AI for Marketing and Sales Optimization
03/11/2024Document and Image Information Extraction
03/11/2024AI for Supply Chain and Logistics Optimization
£4,500.00
Overview:
This course empowers participants to leverage AI in optimizing supply chain and logistics functions, covering demand forecasting, inventory management, route optimization, predictive maintenance, and sustainability. By integrating the latest advancements in AI and IoT, participants will gain practical skills to enhance efficiency, reduce costs, and build more resilient and eco-friendly supply chain operations. Real-world case studies and hands-on exercises will reinforce the concepts, enabling participants to apply AI-driven strategies in their organizations effectively.
Program Objectives:
At the end of this program, participants will be able to:
- Understand the role and applications of AI in modern supply chain and logistics operations.
- Utilize machine learning and real-time data from IoT devices for accurate demand forecasting.
- Implement AI techniques for inventory optimization to balance costs and demand variability.
- Develop AI-driven route optimization strategies using real-time analytics for logistical efficiency.
- Apply predictive maintenance to reduce downtime and improve asset reliability.
- Integrate sustainability goals into supply chain optimization to reduce environmental impact.
- Develop a comprehensive, AI-enhanced supply chain strategy that aligns with business and sustainability objectives.
Target Audience:
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- Supply Chain Managers and Professionals
- Logistics Coordinators and Executives
- Operations Managers and Process Improvement Specialists
- Data Analysts and Data Scientists in Supply Chain Roles
- IT Professionals Supporting Supply Chain Systems
- Business Strategists, Sustainability Officers, and Operations Analysts
Program Outline:
Day 1: Demand Forecasting and Real-Time Data Integration
- Introduction to AI-Driven Demand Forecasting in Supply Chains.
- Machine Learning Techniques for Accurate Demand Predictions (e.g., Time Series Analysis).
- Integrating Real-Time IoT Data for Dynamic Forecasting Adjustments.
- Best Practices for Implementing Demand Forecasting Models in Supply Chain Systems.
- Hands-On Exercise: Building a Demand Forecasting Model in Power BI or Tableau.
- Reflection & Review: Group Discussion on Real-Time Demand Forecasting for Supply Chain Responsiveness.
Day 2:Inventory Optimization and Management with AI
- Principles of Inventory Management – Balancing Costs, Demand, and Supply Variability.
- AI Techniques for Inventory Optimization – Safety Stock Calculation, Dynamic Reordering, and Lead Time Management.
- Implementing Just-In-Time (JIT) and Lean Inventory Practices with AI Support.
- Real-World Examples of AI in Inventory Replenishment and Stockout Prevention.
- Hands-On Exercise: Developing an Inventory Optimization Model in Power BI.
- Reflection & Review: Evaluating AI Solutions for Inventory Efficiency and Cost Savings.
Day 3: Route Optimization and Logistics Planning with Real-Time Analytics
- Topic Introduction to Route Optimization for Cost and Time Efficiency.
- AI Algorithms for Route Optimization (Genetic Algorithms, Ant Colony Optimization, and Dijkstra's Algorithm).
- Using Real-Time Data and IoT Devices for Dynamic Route Adjustments.
- Integrating AI-Driven Route Optimization with Transportation Management Systems (TMS).
- Hands-On Exercise: Creating a Route Optimization Model with Real-Time Data Simulation in Power BI.
- Reflection & Review: Analyzing the Benefits and ROI of AI-Enhanced Route Optimization.
Day 4: Predictive Maintenance and IoT Integration for Asset Reliability
- Understanding Predictive Maintenance in Supply Chains – Benefits and Applications.
- Machine Learning Models for Predictive Maintenance – Condition-Based Monitoring and Failure Prediction.
- IoT Integration for Equipment Health Tracking and Automated Alerts.
- Reducing Downtime and Maintenance Costs with Predictive Analytics and Real-Time Monitoring.
- Hands-On Exercise: Building a Predictive Maintenance Model in Tableau.
- Reflection & Review: Discussing the Impact of Predictive Maintenance on Supply Chain Reliability.
Day 5: Sustainability in Supply Chains and Future Trends in AI
- Incorporating AI for Sustainable Supply Chain Practices – Carbon Footprint Reduction and Waste Minimization.
- Using AI to Optimize Resource Allocation, Reduce Energy Use, and Promote Circular Economy Models.
- Real-World Case Studies of Sustainable AI-Driven Supply Chains.
- Future Trends in AI for Supply Chains – Autonomous Vehicles, Warehouse Robotics, and Proactive Analytics.
- Capstone Project: Developing an AI-Enhanced Supply Chain Strategy Integrating Demand Forecasting, Inventory Optimization, Route Planning, Predictive Maintenance, and Sustainability.
- Reflection & Review: Final Project Presentations, Peer Feedback, and Group Discussion on Emerging AI Trends.