Online Course – Certified Professional Internship in Data Science for Improving Supply Chain Resilience – University of California, Irvine

Internship – 3-part course series

Over the past two decades, supply chains have become more complex. While advanced technology has allowed companies to collect a wealth of data, they have not been able to keep up with the pace required to analyze and extract insights from that data. This specialization includes hands-on activities that demonstrate how data science techniques can transform raw data into decision-making tools for a more agile supply chain.

Basic techniques

• Demand forecast
• Inventory management with demand variability
• Using the seller’s model to determine price

Advanced techniques

• Capacity and resource optimization
• Risk reduction using Monte Carlo simulation

By the end of the internship, you will be able to:

• Describe how demand planning, supply planning, and constrained forecasts are related to each other.
• Use Excel to analyze historical data and assess future needs.
• Analyze historical data to determine inventory levels in situations of constant and uncertain demand using Excel.
• Manage inventory in an unsafe environment.
• Determine inventory needs for items in one period using the seller model.
• Identify the components of capacity optimization, resource optimization, and Monte Carlo simulation.
• Establish and solve optimization problems in Excel.
• Create a demand and inventory view and run a Monte Carlo simulation to improve the supply chain.

Hands-on Learning Project

During the internship, learners work with real supply chain data to analyze different scenarios. Finally, learners apply the concepts from the previous two courses to apply the data science skills they have learned to improve supply chain profits.