The Machine Learning for Demand Forecasting in Vienna is a professional training course designed to help participants apply machine learning models to predict demand and optimize business operations.
From:
20-07-2026
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 Vienna equip professionals with the knowledge and practical skills to leverage machine learning techniques for accurate demand prediction, inventory optimization, and strategic business planning. Designed for data analysts, supply chain managers, business planners, and operations leaders, these programs focus on applying predictive analytics to improve operational efficiency, reduce costs, and enhance decision-making across industries.
Participants gain a thorough understanding of machine learning methods for demand forecasting, including regression analysis, time series modeling, neural networks, and ensemble techniques. The courses explore how these models can be applied to forecast product demand, anticipate market trends, and optimize resource allocation. Through hands-on exercises and real-world case studies, attendees learn to preprocess data, select appropriate algorithms, evaluate model performance, and integrate predictive insights into business planning processes.
These demand forecasting and machine learning training programs in Vienna also emphasize practical applications within supply chain management, inventory control, and sales planning. Participants learn how to combine historical data, external market signals, and real-time inputs to build robust forecasting models. The curriculum also covers model validation, error analysis, and scenario planning to ensure forecasts are accurate, reliable, and actionable.
Attending these training courses in Vienna provides a unique opportunity to collaborate with industry experts and peers from diverse sectors in a city recognized for innovation and technology. Vienna’s dynamic business environment enhances the learning experience, offering exposure to advanced data analytics and AI-driven operational strategies. By completing this specialization, participants will be equipped to implement machine learning models for demand forecasting, improve supply chain efficiency, and make informed, data-driven decisions that support organizational growth and competitiveness in rapidly changing markets.