Forecasting in Supply Chain Management

Forecasting in Supply Chain Management equips participants with essential skills to analyze and predict demand trends using advanced quantitative methods. The course emphasizes practical applications through project-based learning, allowing participants to engage directly with real-world data sets and forecasting tools. By the end of the program, learners will gain a comprehensive understanding of various forecasting techniques and their implications for supply chain efficiency.
Participants will explore a range of methodologies, from time series analysis to machine learning applications in forecasting. The curriculum includes hands-on projects that encourage collaboration and innovation, culminating in a final project where learners will develop a forecasting model tailored to a specific supply chain scenario. Successful completion of the course will prepare participants to make data-driven decisions that enhance operational performance and strategic planning in supply chain contexts.
Introduction to Supply Chain Dynamics
Overview of Forecasting Techniques
Time Series Analysis: Concepts and Applications
Regression Analysis for Demand Forecasting
Seasonal and Trend Analysis in Data
Machine Learning Approaches to Forecasting
Utilizing Software Tools for Forecasting (e.g., R, Python)
Case Studies in Supply Chain Forecasting
Developing a Comprehensive Forecasting Model
Final Project: Real-world Application of Forecasting Techniques

To equip participants with the skills necessary to create and implement effective forecasting models in supply chain management.

Graduates, job seekers, business professionals, and optionally researchers or consultants.

Basic understanding of statistics and data analysis.