The Machine Learning for Demand Forecasting course in Madrid is designed to provide professionals with the skills to use machine learning models for accurate demand predictions and supply chain optimization.
From:
25-05-2026
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 Madrid provide professionals with advanced skills to leverage predictive analytics and machine learning techniques for accurate demand planning and operational optimization. Designed for supply chain managers, data analysts, business strategists, and operations professionals, these programs focus on translating complex data into actionable insights that enhance inventory management, resource allocation, and business performance.
Participants explore a wide range of machine learning approaches for forecasting, including time series analysis, regression models, neural networks, and ensemble methods. The courses emphasize how these techniques can predict demand patterns, detect anomalies, and optimize decision-making across sales, production, logistics, and procurement functions. Through hands-on exercises, case studies, and simulation exercises, attendees learn to develop, validate, and deploy predictive models that support both short-term and strategic planning objectives.
These demand forecasting and machine learning training programs in Madrid blend theoretical foundations with practical applications, covering data preprocessing, feature engineering, model evaluation, and visualization. Participants gain experience integrating machine learning outputs into planning dashboards, automated reporting systems, and scenario analysis frameworks, enabling more responsive and data-driven operational strategies. The curriculum also highlights emerging trends such as AI-enhanced forecasting, real-time predictive analytics, and optimization algorithms that enhance supply chain resilience and agility.
Attending these training courses in Madrid offers a dynamic learning environment enriched by expert instructors and international professional perspectives. Madrid’s innovative business ecosystem provides a unique backdrop for exploring advanced predictive analytics applications and collaborative problem-solving. By completing this specialization, participants will be equipped to leverage machine learning for precise demand forecasting, improve operational efficiency, and drive strategic business decisions—ensuring organizations remain agile, competitive, and data-driven in an increasingly complex global marketplace.