The Machine Learning for Demand Forecasting in Brussels is a practical training course designed to equip professionals with skills to leverage ML techniques for accurate demand prediction and business planning.
From:
04-05-2026
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.
The Machine Learning for Demand Forecasting Training Courses in Brussels equip professionals with advanced analytical capabilities to anticipate market needs, optimize resource allocation, and make strategic business decisions based on data-driven insights. Designed for supply chain managers, data analysts, business planners, and operations leaders, these programs focus on how machine learning models can improve forecasting accuracy and support smarter planning in dynamic business environments.
Participants explore the core concepts of demand forecasting, including time-series analysis, regression models, predictive analytics, and scenario planning. The courses emphasize how machine learning algorithms can detect patterns in historical data, incorporate real-time variables, and adjust forecasts with greater precision than traditional methods. Through practical workshops and case studies, attendees learn to prepare datasets, select appropriate model structures, evaluate model performance, and translate outputs into actionable business strategies.
These demand forecasting and machine learning training programs in Brussels highlight applications across various industries, from supply chain management and retail planning to manufacturing, logistics, and financial operations. Participants gain hands-on experience with forecasting tools and automation platforms that support scalable, accurate, and continuous forecasting workflows. The curriculum also addresses key considerations such as data quality, seasonality, external market drivers, and the integration of forecasting models into enterprise planning systems.
Attending these training courses in Brussels offers a valuable opportunity to engage with international experts and peers in a collaborative learning environment. The city’s dynamic business ecosystem provides a rich context for exploring innovation in forecasting strategies and data-driven decision-making. By completing this specialization, participants will be equipped to design and implement machine learning forecasting models, improve operational planning, reduce uncertainty, and enhance organizational responsiveness—empowering their businesses to stay competitive and agile in changing market conditions.