Executive Development Programme in Supply Chain Demand Forecasting Methods

This course is tailored for supply chain managers, logistics directors, and data analysts seeking to enhance their forecasting skills. Participants will gain proficiency in advanced demand forecasting methods, including time series analysis, machine learning techniques, and predictive analytics tools. The curriculum equips learners with the ability to implement these methods to improve inventory management and reduce costs.

Upon completion, attendees will be able to apply cutting-edge forecasting models to real-world scenarios, making informed decisions to optimize supply chain performance and meet customer demands more effectively.

1. 1. Introduction to Supply Chain Demand Forecasting: Learners will understand the basics of supply chain demand forecasting, including its importance and key forecasting techniques. They will gain foundational knowledge on how to approach demand forecasting and the importance of accuracy in supply chain management.
2. 2. Data Collection and Analysis for Forecasting: This module covers the methods for gathering and analyzing data essential for accurate forecasting. Learners will learn how to use historical data effectively and apply statistical techniques to extract meaningful insights.
3. 3. Time Series Forecasting Techniques: Learners will explore various time series forecasting methods such as moving averages, exponential smoothing, and ARIMA. They will gain practical skills in using these techniques to predict future demand trends.
4. 4. Advanced Forecasting Models: This module delves into more complex models like Seasonal Decomposition of Time Series by Loess (STL), state-space models, and machine learning-based forecasting. Learners will understand how to select and implement appropriate models for different scenarios.
5. 5. Forecast Error Measurement and Model Evaluation: Learners will study how to measure forecast accuracy using metrics like MAE, RMSE, and MAPE. They will learn to evaluate the performance of forecasting models and make necessary adjustments to improve accuracy.
6. 6. Demand Forecasting in Disrupted Environments: This module focuses on forecasting in volatile and uncertain conditions. Learners will learn how to handle disruptions and incorporate them into their forecasting models to maintain supply chain resilience.
7. 7. Integrated Demand Forecasting with Supply Chain Strategy: Learners will explore the integration of demand forecasting with broader supply chain strategies. They will understand how to align demand forecasts with inventory management, production planning, and supplier relationship management.
8. 8. Forecasting Tools and Software: This module introduces popular forecasting tools and software. Learners will gain hands-on experience with tools like Excel, R, Python, and specialized software such as IBM SPSS Forecasting.
9. 9. Ethical Considerations in Demand Forecasting: Learners will discuss the ethical implications of demand forecasting, including privacy concerns, data bias, and the impact of automated systems on workforce. They will learn to apply ethical standards in their forecasting practices.
10. 10. Strategic Decision-Making with Demand Forecasts: This final module equips learners with the skills to use demand forecasts to make strategic decisions. They will learn how to leverage forecasts for capacity planning, inventory optimization, and demand-driven supply chain design.