Data Analytics, Artificial Intelligence and Decision Sciences
Forecasting Demand with Machine Learning Techniques
Please select a city/session before registration.
About this program
Precise demand forecasting plays a crucial role in enhancing supply chain performance, optimizing inventory levels, and supporting strategic decision-making. This Machine Learning for Demand Forecasting Training Course equips participants with advanced ML methodologies that surpass traditional forecasting approaches. Attendees will acquire skills to develop and assess forecasting models, utilize time-series analysis, and implement both supervised and unsupervised learning techniques. Through practical labs and real-world case studies, learners will understand how organizations employ ML to predict demand, minimize costs, and enhance decision-making processes. Upon completing the course, participants will be capable of designing and deploying machine learning models that provide more dependable demand forecasts and facilitate adaptive business strategies.
Course benefits
- Enhance demand forecast precision using machine learning
- Leverage predictive analytics for improved planning
- Streamline supply chain and inventory control
- Forecast customer demand and market trends effectively
- Bolster decision-making through AI-driven insights
Key outcomes
- Investigate machine learning applications in demand forecasting
- Develop forecasting models based on time-series and regression techniques
- Implement supervised and unsupervised machine learning methods
- Assess and validate the accuracy and reliability of models
- Utilize ML to predict supply chain and sales demand
- Tackle challenges related to data quality and feature engineering
- Incorporate ML forecasting solutions into business planning systems
Who should attend
- Supply chain and operations managers
- Data scientists and analysts
- Business strategists and planners
- Professionals in retail, manufacturing, and logistics sectors
Course outline
Unit 1: Overview of Machine Learning Applications in Forecasting
- Comparing traditional forecasting techniques with machine learning methods
- Advantages and obstacles of applying ML in demand forecasting
- Fundamental ML algorithms used for prediction
- Case studies from various industries
Unit 2: Data Processing and Feature Creation
- Acquiring and preprocessing demand datasets
- Managing missing data points and anomalies
- Developing features to enhance predictive accuracy
- Hands-on exercise in dataset preparation
Unit 3: Time Series Analysis and Predictive Model Development
- Exploring time-series methods including ARIMA models
- Applying regression and neural network techniques
- Combining models for sophisticated forecasting
- Practical steps to construct predictive models
Unit 4: Assessing and Validating Forecasting Models
- Performance metrics for evaluating forecast precision
- Methods for cross-validation and model testing
- Strategies to prevent overfitting and underfitting
- Case study on evaluating models in real-world scenarios
Unit 5: Integrating ML Forecasts into Business and Emerging Trends
- Incorporating ML-based forecasts within supply chain management
- Leveraging forecasting for optimizing sales and inventory
- Ethical and regulatory issues in AI-driven forecasting
- Upcoming innovations in demand forecasting technology