Overview:
Data-Driven Supply Chain Strategy is a specialized field within the broader domain of data analytics and supply chain management. It focuses on the application of data analysis, statistical techniques, and models to optimize and enhance various aspects of a company's supply chain operations. Predictive analytics models are applied to identify risk assessments with the potential to disrupt the supply chain, such as natural disasters, geopolitical instability, suppliers, or unexpected changes in demand. Students will have the tools to develop risk mitigation strategies and build resilience into the supply chain by identifying vulnerabilities and implementing strategies to recover quickly from disruptions. Incorporating risk management into supply chain analytics is critical in today's volatile business environment. It enables companies to proactively address potential disruptions, minimize financial losses, maintain business continuity, and support customer trust.
Program Goal and Outcomes
GOAL
Students will gain a comprehensive understanding of the global supply chain, using analytical tools, to equip them with the knowledge and skills necessary to make data-driven decisions.
OUTCOMES
1. Perform data analysis and visualization within a problem-solving process framework.
2. Use linear and integer programming to model various scenarios.
3. Identify concepts, techniques, and decision tools available to manage an organization's supply chain.
4. Examine current trends and challenges in global sourcing.
5. Use a risk management framework and maturity model to assess and mitigate supply chain risks in global organizations.
Degree Requirements
- A baccalaureate degree from a regionally accredited institution
- A minimum cumulative GPA of 2.5
Admission Requirements
Information about admission requirements and application materials is available on the Graduate and Continuing Studies website.
Tuition and Fees
For information on tuition and fees, please visit the Graduate and Continuing Studies website.