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Course Overview

Accurate demand forecasting is vital for supply chain efficiency, inventory optimization, and strategic planning. This Machine Learning for Demand Forecasting Training Course introduces participants to modern ML techniques that outperform traditional forecasting methods.

Participants will learn how to build and evaluate forecasting models, use time-series analysis, and apply supervised and unsupervised learning approaches. Real-world case studies and practical labs will show how organizations leverage ML to anticipate demand, reduce costs, and improve decision-making.

By the end of the course, attendees will be able to design and implement machine learning models that deliver more reliable demand forecasts and support agile business strategies.

Course Benefits

• Improve demand forecasting accuracy with ML

• Apply predictive analytics for smarter planning

• Optimize supply chain and inventory management

• Anticipate customer demand and market fluctuations

• Strengthen decision-making with AI-driven insights

Course Objectives

• Explore machine learning applications in demand forecasting

• Build time-series and regression-based forecasting models

• Apply supervised and unsupervised ML techniques

• Evaluate and validate model performance

• Use ML for supply chain and sales demand predictions

• Address data quality and feature engineering challenges

• Integrate ML forecasting into business planning systems

Training Methodology

The course combines lectures, case studies, and hands-on labs with forecasting datasets. Participants will build and test ML models using real-world scenarios and platforms.

Target Audience

• Supply chain and operations managers

• Data scientists and analysts

• Business strategists and planners

• Professionals in retail, manufacturing, and logistics

Target Competencies

• Machine learning forecasting techniques

• Predictive analytics for demand planning

• Time-series modeling and evaluation

• Data-driven supply chain strategy

Course Outline

Unit 1: Introduction to ML in Forecasting

• Traditional vs. machine learning forecasting methods

• Benefits and challenges of ML in demand planning

• Key ML algorithms for forecasting

• Industry case studies

Unit 2: Data Preparation and Feature Engineering

• Collecting and cleaning demand data

• Handling missing values and outliers

• Feature engineering for better predictions

• Practical dataset preparation exercise

Unit 3: Time-Series and Predictive Modeling

• Time-series analysis and ARIMA models

• Regression and neural network approaches

• Hybrid models for complex forecasting

• Building predictive models in practice

Unit 4: Model Evaluation and Validation

• Metrics for forecasting accuracy

• Cross-validation and testing approaches

• Avoiding overfitting and underfitting

• Real-world model evaluation case study

Unit 5: Business Integration and Future of ML Forecasting

• Embedding ML forecasts into supply chain planning

• Using forecasts for sales and inventory optimization

• Ethical and governance considerations in AI forecasting

• Future trends in demand forecasting technologies

Ready to improve forecasting with machine learning?
Join the Machine Learning for Demand Forecasting Training Course with EuroQuest International Training and transform the accuracy of your business planning.