This presentation covers a mathematical model of supply chain management and practical applications for this model in the real world. Fully applying the theory behind this research is not expected.

The presentation covers the following components:

• Brief overview of key tenets of successful supply chain management
• Brief overview of frequentist, propensity, and bayesian statistics and the foundational differences in arrival at a conclusion of ground truth
• Overview for how a bayesian model seeks truth from observations and how this applies to supply chain management
• How disaster recovery plans and failure cost predictions are created and how they factor into inventory levels
• Applying these statistics to create a model that covers supply chain risk, forecasting, consumption variability, weather, geopolitical turmoil, etc.
• An actual example of how this system is implemented, i.e. moving beyond concepts