Data Analytics for Supply Chain Optimization

Data Analytics for Supply Chain Optimization equips participants with the analytical skills necessary to enhance supply chain performance through data-driven decision-making. This course emphasizes practical applications of data analytics, enabling learners to tackle real-world challenges faced in supply chain management. By engaging in project-based learning, participants will not only develop technical expertise but also gain insights into how data can transform supply chain processes.
The curriculum covers a range of topics essential for mastering data analytics in a supply chain context. Participants will explore advanced analytical tools, data visualization techniques, and predictive modeling, all aimed at optimizing inventory management, logistics, and supplier relationships. The final project will involve a comprehensive analysis of a supply chain case study, where learners will apply their acquired skills to propose actionable solutions. This hands-on approach ensures that graduates are well-prepared to meet the demands of the job market.
Introduction to Data Analytics in Supply Chain
Key Performance Indicators (KPIs) for Supply Chain Management
Data Collection Techniques and Tools
Data Cleaning and Preprocessing Methods
Exploratory Data Analysis (EDA) for Supply Chain Data
Predictive Analytics and Forecasting Techniques
Optimization Models for Inventory Management
Data Visualization Best Practices
Case Studies in Supply Chain Analytics
Final Project: Comprehensive Supply Chain Data Analysis

Equip participants with the skills to analyze and optimize supply chain processes through data analytics techniques.

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

Basic understanding of data analytics and supply chain principles; familiarity with Excel and statistical software is recommended.