Course Overview
This course is designed to equip supply chain professionals with the knowledge and skills needed to leverage big data analytics for optimizing supply chain operations. Participants will learn how to harness the power of big data to make data-driven decisions, improve efficiency, reduce costs, and enhance overall supply chain performance. The course covers fundamental concepts of big data analytics, advanced analytical techniques, and practical applications in the context of supply chain management.
Course Duration
5 Days
Who Should Attend
- Supply Chain Managers
- Operations Managers
- Logistics Managers
- Data Analysts
- IT Professionals working in supply chain management
- Business Analysts
- Professionals involved in supply chain strategy and optimization
- Anyone interested in understanding the impact of big data on supply chain operations
Course Objectives
By the end of this course, participants will be able to
- Understand the basics of big data and its relevance to supply chain management.
- Apply big data analytics techniques to optimize various aspects of the supply chain.
- Utilize data visualization tools to interpret and present data insights.
- Implement predictive analytics for forecasting demand and managing inventory.
- Enhance decision-making processes using data-driven insights.
- Identify and mitigate risks in the supply chain through advanced analytics.
- Improve supply chain efficiency and reduce operational costs using big data.
- Develop and manage big data projects within the supply chain context. receiving, storage, and distribution processes.
Course Outline:
Module 1: Introduction to Big Data and Supply Chain Management
- Overview of Big Data and its significance
- Key components of the supply chain
- Integration of big data in supply chain management
- Case studies on big data applications in supply chains
Module 2: Data Collection and Management
- Sources of big data in supply chains
- Data collection techniques and tools
- Data storage and management strategies
- Ensuring data quality and integrity
Module 3: Data Analysis Techniques
- Descriptive analytics: tools and methods
- Predictive analytics: techniques and applications
- Prescriptive analytics: optimization models
- Hands-on exercises using analytical tools
Module 4: Data Visualization and Interpretation
- Importance of data visualization
- Tools for data visualization (Tableau, Power BI, etc.)
- Creating and interpreting dashboards and reports
- Practical exercises in data visualization
Module 5: Practical Applications and Case Studies
- Real-world applications of big data in supply chains
- Case studies and best practices
- Developing a big data strategy for supply chain optimization
- Analyzing and presenting a supply chain optimization case
