Supply Chain Management Courses

Week 1: Introduction to Supply Chain Management and Analytics (4 Hours)

Session 1 (2 Hours): Fundamentals of Supply Chain Management

Overview of Supply Chain Concepts and Components

Role of Analytics in Supply Chain Management

Introduction to Supply Chain Models

Session 2 (2 Hours): Introduction to Supply Chain Analytics Tools and Techniques

Overview of Analytical Tools Used in Supply Chain (e.g., Excel, R, Python)

Basic Data Analysis Techniques for Supply Chain Data

Case Studies Highlighting the Importance of Analytics in Supply Chain

Week 2: Data Management and Descriptive Analytics (6 Hours)

Session 3 (2 Hours): Data Management in Supply Chains

Data Collection and Management Strategies in Supply Chains

Data Quality and Governance in Supply Chain Analytics

Introduction to Data Warehousing and Data Lakes

Session 4 (2 Hours): Descriptive Analytics in Supply Chain

Using Descriptive Analytics to Understand Historical Performance

Key Performance Indicators (KPIs) in Supply Chain Management

Visualization Techniques for Supply Chain Data

Session 5 (2 Hours): Practical Exercise on Descriptive Analytics

Hands-on Exercise with a Supply Chain Dataset

Creating Dashboards and Reports for Supply Chain Performance

Week 3: Predictive Analytics and Optimization Techniques (6 Hours)

Session 6 (2 Hours): Introduction to Predictive Analytics in Supply Chain

Overview of Predictive Modeling Techniques

Forecasting Demand and Inventory Requirements

Predictive Maintenance in Supply Chain Operations

Session 7 (2 Hours): Supply Chain Optimization Techniques

Linear Programming and Network Optimization Models

Optimization of Logistics and Distribution Networks

Case Study on Supply Chain Optimization

Session 8 (2 Hours): Advanced Topics in Predictive Analytics

Machine Learning Applications in Supply Chain

Scenario Planning and Risk Analysis in Supply Chain

Real-time Analytics and IoT in Supply Chain Management

Week 4: Strategic Applications and Capstone Project (4 Hours)

Session 9 (2 Hours): Integrating Analytics into Supply Chain Strategy

Building Data-Driven Supply Chain Strategies

Sustainability and Ethics in Supply Chain Analytics

Future Trends in Supply Chain Analytics

Session 10 (2 Hours): Capstone Project and Course Wrap-Up

Group Project: Developing an Analytical Solution for a Supply Chain Problem

Presentations of Capstone Projects

Course Summary and Pathways for Further Learning

The course should be a mix of lectures, case studies, and practical exercises, ideally using real-world supply chain data. The capstone project in the final week would allow students to apply the concepts and techniques learned to a real or simulated supply chain problem, reinforcing their understanding and practical skills in supply chain analytics. This structure ensures that MBA students are not only knowledgeable about analytical techniques but also understand how to apply these skills effectively in the context of supply chain management.

Week 1: Introduction to Supply Chain Management and Analytics (4 Hours)

Session 1 (2 Hours): Fundamentals of Supply Chain Management

Overview of Supply Chain Concepts and Components

Role of Analytics in Supply Chain Management

Introduction to Supply Chain Models

Session 2 (2 Hours): Introduction to Supply Chain Analytics Tools and Techniques

Overview of Analytical Tools Used in Supply Chain (e.g., Excel, R, Python)

Basic Data Analysis Techniques for Supply Chain Data

Case Studies Highlighting the Importance of Analytics in Supply Chain

Week 2: Data Management and Descriptive Analytics (6 Hours)

Session 3 (2 Hours): Data Management in Supply Chains

Data Collection and Management Strategies in Supply Chains

Data Quality and Governance in Supply Chain Analytics

Introduction to Data Warehousing and Data Lakes

Session 4 (2 Hours): Descriptive Analytics in Supply Chain

Using Descriptive Analytics to Understand Historical Performance

Key Performance Indicators (KPIs) in Supply Chain Management

Visualization Techniques for Supply Chain Data

Session 5 (2 Hours): Practical Exercise on Descriptive Analytics

Hands-on Exercise with a Supply Chain Dataset

Creating Dashboards and Reports for Supply Chain Performance

Week 3: Predictive Analytics and Optimization Techniques (6 Hours)

Session 6 (2 Hours): Introduction to Predictive Analytics in Supply Chain

Overview of Predictive Modeling Techniques

Forecasting Demand and Inventory Requirements

Predictive Maintenance in Supply Chain Operations

Session 7 (2 Hours): Supply Chain Optimization Techniques

Linear Programming and Network Optimization Models

Optimization of Logistics and Distribution Networks

Case Study on Supply Chain Optimization

Session 8 (2 Hours): Advanced Topics in Predictive Analytics

Machine Learning Applications in Supply Chain

Scenario Planning and Risk Analysis in Supply Chain

Real-time Analytics and IoT in Supply Chain Management

Week 4: Strategic Applications and Capstone Project (4 Hours)

Session 9 (2 Hours): Integrating Analytics into Supply Chain Strategy

Building Data-Driven Supply Chain Strategies

Sustainability and Ethics in Supply Chain Analytics

Future Trends in Supply Chain Analytics

Session 10 (2 Hours): Capstone Project and Course Wrap-Up

Group Project: Developing an Analytical Solution for a Supply Chain Problem

Presentations of Capstone Projects

Course Summary and Pathways for Further Learning

The course should be a mix of lectures, case studies, and practical exercises, ideally using real-world supply chain data. The capstone project in the final week would allow students to apply the concepts and techniques learned to a real or simulated supply chain problem, reinforcing their understanding and practical skills in supply chain analytics. This structure ensures that MBA students are not only knowledgeable about analytical techniques but also understand how to apply these skills effectively in the context of supply chain management.