This course is the third in the four-course Supply Chain Analytics Professional certificate program. It introduces the field of machine learning, an area where algorithms learn patterns from data to support proactive decision making, as it applies to supply chain management. You’ll use machine learning to conduct predictive analytics as you forecast future demand, develop inventory policies, perform customer segmentation and predictive maintenance. You’ll use Python and PowerBI to create and analyze regression, clustering, and classification models.
Who Should Attend
Experienced business professionals who perform or want to perform analytics to improve their supply chain management processes. They want to tackle strategic goals and to perform leading edge analytics projects that address the full complexity of supply chains.
How You Will Benefit
- Understand the role of machine learning (ML) in Supply Chain Management (SCM)
- Apply advanced analytics techniques to build planning tools that can leverage large and real-time data sets
- Apply ML in demand forecasting and predictive maintenance
- Understand how to assess ML model performance, improve models, and pick the best model for a decision
- Use Python and PowerBI to build, analyze, and deploy ML models
What Is Covered
- How ML relates to SCM
- ML algorithms such as regression trees, clustering techniques, decision trees, random forests, logistic regression
- Aspects of ML projects including parameter tuning, cross validation, and assess model performance
- Application of ML in demand forecasting for sales and operations planning (S&OP) and inventory management
- Application of ML in predictive maintenance
- Hands-on practice with these skills using data from the (fictional) Cardboard Company (CBC)