Supply Chain Analytics: Driving Efficiency and Innovation
FV
This e-learning course provides a comprehensive introduction to the principles and applications of supply chain analytics. Participants will learn how data analytics can be leveraged to optimize supply chain operations, enhance decision-making, reduce costs, and improve overall business performance. Through real-world case studies, the course highlights practical techniques that have been successfully implemented by leading companies to gain a competitive advantage.
Course Objectives:
By the end of this course, participants will be able to:
- Understand the fundamental concepts of data analytics and how they apply to supply chains.
- Distinguish between descriptive, diagnostic, predictive, and prescriptive analytics.
- Identify key data sources used in supply chain analytics and their roles in decision-making.
- Analyze real-world case studies to understand the tangible benefits of supply chain analytics.
- Apply best practices and methodologies to solve common supply chain challenges using data-driven insights.
- Explore future trends in supply chain analytics, including AI, IoT, and blockchain technologies.
Course Outline:
Module 1: Introduction to Supply Chain Analytics
- Definition and significance of supply chain analytics
- Overview of the four types of analytics: Descriptive, Diagnostic, Predictive, and Prescriptive
Module 2: Key Data Sources in Supply Chain Analytics
- ERP systems, IoT devices, customer demand data, and supplier performance metrics
- Information-action value chain: How data translates into decisions
Module 3: Types of Analytics in Supply Chain
- Descriptive Analytics: Understanding historical trends
- Diagnostic Analytics: Identifying the root cause of issues
- Predictive Analytics: Forecasting future trends and demand
- Prescriptive Analytics: Recommending actions to optimize supply chain performance
Module 4: Real-World Use Cases in Supply Chain Analytics
- Walmart: Inventory optimization
- Amazon: Demand forecasting
- UPS: Route optimization
- PepsiCo: Demand planning and inventory management
- Siemens: Predictive maintenance in manufacturing
Module 5: Best Practices and Future Trends in Supply Chain Analytics
- Leveraging AI, IoT, and blockchain technologies
- Adopting a data-driven approach for continuous improvement
Module 6: Conclusion and Reflections
- Key takeaways and reflections from case studies
- Discussion of the future landscape of supply chain analytics
Target Participants:
This course is designed for:
- Supply chain managers and professionals looking to enhance their decision-making processes using data.
- Data analysts and IT professionals who want to specialize in supply chain analytics.
- Operations managers and executives interested in optimizing business performance through data-driven insights.
- Business students and professionals aspiring to develop skills in analytics and supply chain management.
- Individuals interested in learning how analytics can transform supply chain operations and provide a competitive edge.
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very informative
Supply Chain Analytics: Driving Efficiency and Innovation In today's fast-paced global economy, businesses are constantly seeking ways to optimize their supply chains. Supply chain analytics, a powerful tool that leverages data to drive informed decision-making, has emerged as a critical component of modern business strategy. At its core, supply chain analytics involves collecting, cleaning, and analyzing vast amounts of data related to various aspects of the supply chain, including procurement
The topic caught my interest in supply chain analytics. It provides lot of knowledge about supply chain analytics and how it is very important to utilize this to improve the services of a company.
Good way of teaching
I enjoyed it and learn a lot
AM
KM
NP