Executive Development Programme in Demand Planning with Machine Learning

This course is designed for executives and senior managers seeking to enhance their strategic decision-making in demand planning through the integration of machine learning. Participants will learn to leverage advanced analytics and predictive models to forecast demand more accurately, optimize inventory, and reduce costs.

By the end of the program, attendees will gain practical skills in using machine learning algorithms for demand forecasting, understand the impact of data quality on predictive models, and develop a strategic approach to implementing machine learning solutions within their organizations.

1. 1. Introduction to Demand Planning: Learners will understand the basics of demand planning and its importance in supply chain management. They will gain foundational knowledge on how to forecast demand using traditional methods.
2. 2. Data Fundamentals for Demand Planning: This module covers essential data handling and preparation techniques necessary for demand planning. Learners will learn to clean and preprocess data, enabling them to build accurate demand forecasts.
3. 3. Machine Learning Basics for Demand Forecasting: Introduces the fundamental concepts of machine learning and its application in demand forecasting. Learners will be able to understand and implement basic machine learning models for forecasting.
4. 4. Advanced Machine Learning Models for Demand Forecasting: Focuses on more sophisticated machine learning models such as ARIMA, Prophet, and neural networks. Learners will gain the skills to select and apply appropriate models for complex forecasting scenarios.
5. 5. Feature Engineering for Demand Planning: Teaches how to create meaningful features from raw data to improve forecast accuracy. Learners will learn to engineer features that capture key historical and contextual information.
6. 6. Time Series Analysis Techniques: Explores advanced time series analysis techniques to handle seasonal patterns, trends, and other complexities in demand data. Learners will be able to analyze and model time series data effectively.
7. 7. Integration of Machine Learning with Business Intelligence: Discusses how to integrate machine learning models with business intelligence tools for real-time demand forecasting and decision-making. Learners will learn to leverage BI tools for model deployment and monitoring.
8. 8. Demand Planning Optimization Techniques: Covers optimization techniques to refine demand forecasts and improve planning processes. Learners will gain skills in using optimization algorithms to enhance forecast accuracy and plan resources efficiently.
9. 9. Demand Planning Case Studies: Analyzes real-world case studies to understand practical challenges and solutions in demand planning. Learners will learn from diverse industry examples and apply best practices to their own scenarios.
10. 10. Leadership and Strategic Thinking in Demand Planning: Focuses on developing leadership and strategic thinking skills for demand planning professionals. Learners will learn to lead teams, make strategic decisions, and drive organizational success through effective demand planning.