Course Overview:
Final Project: AI Strategy Proposal for Your Business
Participants will develop an AI strategy proposal for a specific business problem, including use cases, tools, and implementation steps, based on their learnings throughout the course.
Learning Outcomes:
By the end of this course, participants will:
- Understand the fundamental concepts of AI and its applications in business.
- Be able to identify key AI technologies and their use cases.
- Learn how to use AI for decision-making, customer engagement, and operational efficiency.
- Gain practical insights on how to implement AI strategies in their organizations.
Curriculum
- 7 Sections
- 0 Lessons
- 6 Weeks
Expand all sectionsCollapse all sections
- Week 1: Introduction to Machine Learning and Supply ChainSession 1: Overview of Supply Chain Processes Key components: Procurement, Manufacturing, Distribution, Retail, and Logistics Challenges in modern supply chains Session 2: Introduction to Machine Learning Types of machine learning: Supervised, Unsupervised, and Reinforcement Learning Applications of ML in supply chain management Tools and libraries: Python, Scikit-learn, TensorFlow, Keras0
- Week 2: Data Collection, Cleaning, and Feature EngineeringSession 3: Supply Chain Data Sources ERP systems, IoT sensors, Sales and Inventory data Data collection challenges in supply chain Session 4: Data Preprocessing and Feature Engineering Handling missing data, categorical data encoding, and feature scaling Feature selection techniques for time series data in supply chain0
- Week 3: Demand Forecasting with Machine LearningSession 5: Introduction to Demand Forecasting Importance of accurate demand forecasting in supply chain optimization Traditional vs ML approaches for demand forecasting Session 6: Time Series Forecasting Models ARIMA, Exponential Smoothing, Prophet Hands-on: Build a demand forecasting model using Python0
- Week 4: Inventory Management and OptimizationSession 7: Inventory Optimization with ML Economic Order Quantity (EOQ), Reorder Point (ROP), Safety Stock levels ML-based optimization for dynamic inventory control Session 8: Predictive Analytics for Inventory Management Use of regression models, decision trees, and ensemble methods for inventory forecasting Case study: Inventory management with historical data0
- Week 5: Supply Chain Network OptimizationSession 9: Route Optimization with ML Vehicle Routing Problem (VRP) and Traveling Salesman Problem (TSP) Solving routing problems using optimization algorithms and ML Session 10: Supply Chain Network Design Predicting optimal locations for warehouses, distribution centers Hands-on: Build a network optimization model0
- Week 6: Advanced Applications of Machine Learning in Supply ChainSession 11: Anomaly Detection and Risk Management Detecting fraud, disruptions, and quality issues in supply chain using ML Use of clustering, outlier detection algorithms, and anomaly detection Session 12: Reinforcement Learning for Supply Chain Optimization Introduction to RL for decision-making in real-time supply chain environments Case study: RL applications in warehouse management, transportation0
- Final Project (Week 6)Design and implement a machine learning model for one of the following: Demand forecasting for a given product line Inventory optimization for a retail store Route optimization for a logistics provider0
Requirements
- Prerequisites: Basic knowledge of supply chain management Fundamentals of statistics and linear algebra Basic programming skills in Python Familiarity with Machine Learning concepts
- Programming Language: Python
- Libraries: Scikit-learn, Pandas, NumPy, TensorFlow, Keras, Pyomo, Gurobi (for optimization)
- Data Visualization: Matplotlib, Seaborn
- Cloud Platforms: AWS Sagemaker, Google Cloud ML
