Machine Learning for Supply Chain Optimization

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  • Level
  • Duration
  • Certificate
  • All Levels
  • 6 weeks (12 sessions, 1.5 hours per session)
  • Bharat Academy Education
  • Certified
  • - KHDA
    - AIAL
    - Royal Institute for Chartered Engineer - RICE (USA)
Course Info
Key Highlights
Target Audience

Course Duration: 6 weeks (12 sessions, 1.5 hours per session)

Prerequisites:
  • Basic knowledge of supply chain management
  • Fundamentals of statistics and linear algebra
  • Basic programming skills in Python
  • Familiarity with Machine Learning concepts
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 provider
Tools & Technologies:
  • 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
Learning Outcomes:
  • Understand the role of ML in solving supply chain challenges.
  • Develop predictive models for demand forecasting and inventory optimization.
  • Implement ML solutions for network design and route optimization.
  • Apply anomaly detection techniques to improve supply chain resilience.
  • Gain hands-on experience with real-world supply chain datasets.
Week 1: Introduction to Machine Learning and Supply Chain
  • Session 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, Keras
Week 2: Data Collection, Cleaning, and Feature Engineering
  • Session 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 chain
Week 3: Demand Forecasting with Machine Learning
  • Session 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 Python
Week 4: Inventory Management and Optimization
  • Session 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 data
Week 5: Supply Chain Network Optimization
  • Session 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 model
Week 6: Advanced Applications of Machine Learning in Supply Chain
  • Session 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, transportation
  • Supply chain professionals seeking to enhance efficiency using data-driven techniques.
  • Data scientists and analysts interested in applying machine learning to logistics and operations.
  • Business managers and executives looking to integrate AI for smarter decision-making in supply chain processes.
  • IT and operations professionals aiming to understand the role of machine learning in supply chain optimization.

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