Executive Development Programme in Supply Chain Forecasting with Data Science

This program is designed for supply chain executives and managers seeking to enhance their strategic forecasting capabilities through data-driven insights. Participants will gain proficiency in leveraging advanced data science techniques for accurate demand forecasting, optimizing inventory management, and improving overall operational efficiency.

Attendees will learn to apply predictive analytics, machine learning models, and big data technologies to real-world supply chain scenarios. They will also develop skills in interpreting complex data sets, making data-informed decisions, and driving organizational growth through improved supply chain performance.

1. 1. Introduction to Supply Chain Forecasting: Learners will understand the basics of supply chain forecasting, including its importance and key components. They will gain foundational knowledge in forecasting techniques and tools.
2. 2. Data Science Fundamentals for Forecasting: This module covers essential data science concepts like data cleaning, analysis, and visualization, preparing learners to apply these skills in forecasting scenarios.
3. 3. Time Series Analysis for Forecasting: Learners will delve into time series analysis, including decomposition, trend analysis, and seasonal adjustments, to predict future demand accurately.
4. 4. Predictive Modeling in Supply Chain Forecasting: This module focuses on building predictive models using statistical and machine learning techniques, enabling learners to forecast demand with high precision.
5. 5. Advanced Forecasting Techniques: Learners will explore advanced forecasting methods such as ARIMA, exponential smoothing, and state space models, enhancing their ability to handle complex forecasting challenges.
6. 6. Data-Driven Decision Making in Supply Chain: This module teaches learners how to use forecast data to make informed decisions about inventory management, production planning, and supplier relationships.
7. 7. Data Integration and Analytics Tools: Learners will learn to integrate and analyze data from various sources using tools like SQL, Python, and R, improving their data handling and analytical skills.
8. 8. Case Studies in Supply Chain Forecasting: Through real-world case studies, learners will apply their knowledge to solve supply chain forecasting problems, gaining practical experience and insights.
9. 9. Ethical Considerations in Forecasting: This module discusses the ethical implications of forecasting, including data privacy, bias, and the impact of forecasts on business operations.
10. 10. Future Trends in Supply Chain Forecasting: Learners will explore emerging trends and technologies in supply chain forecasting, such as AI, IoT, and big data, preparing them for future advancements in the field.