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Learn cutting-edge analytics and skills needed to understand modern supply chain operations, and translate data analysis into informed business decisions. As part of the Rutgers Stackable Business Innovation Program (rSBI), the Supply Chain Analytics Concentration is stackable with the following master's programs: Master of Supply Chain Analytics, MS in Healthcare Analytics and Intelligence, MBA.
For supply chains to run smoothly, organizations need data-driven decision-makers who also have functional knowledge in supply chains. This concentration helps build such well-rounded competence via courses that offer analytical data skills and generalized supply chain domain knowledge, and equip students with basic skills for the modern, data-driven supply chain management practices.
You can take the course listed below as individual classes or as stackable courses towards the completion of a concentration.
This course introduces statistics as applied to managerial problems. Emphasis is placed on conceptual understanding as well as on conducting statistical analyses. Students learn the potential and limitations of statistics, gain hands-on experience using Excel as well as comprehensive packages such as R or SAS. Topics include descriptive statistics, continuous distributions, confidence intervals for means and proportions, and regression analysis. Application areas include finance, operations, and marketing. The course introduces the basic concepts of model-building and its role in rational decision-making. Knowledge of specific modeling techniques, such as linear and nonlinear programming, decision analysis, and simulation, along with some insight into their practical application, are acquired. Students are encouraged to take an analytic view of decision-making by formalizing trade-offs, specifying constraints, providing for uncertainty, and performing sensitivity analyses. Students form groups to collect and analyze data and to write and present a final report.
Risk management is a multidisciplinary field involving finance, economics, mathematics, and computer science. This course covers an introduction to the theory and practice of risk management with an emphasis on techniques and applications. We consider FMEA (Failure Mode Effect Analysis), FTA (Fault Tree Analysis), HACCP (Hazzard Analysis and Critical Point Control), simulation, portfolio optimization, value at risk, and coherent risk measures. This course emphasizes the use of mathematical models to analyze risk phenomena and the implementation of risk-aware solutions. In this course, we follow a mathematical modeling approach to analyze and solve real-life applications in the context of risk. Our main tools are probability and mathematical optimization. The course develops the student's ability to analyze risk-related issues in a wide range of applications central to today’s risk theory and practice. The skills developed in this course can be applied to a broad range of business problems. The examples and student exercises will focus on the following areas: real options, supply chain management, shop floor operation scheduling, project management, and portfolio analysis and optimization.
Business intelligence (BI) is a set of technologies and processes that allow people at all levels of an organization to access, interact with and analyze data. In a data-rich business environment, BI can help a management team to operate efficiently, discover new market opportunities and improve business performance. This course focuses on data science techniques, analytical toolboxes and business applications in supply chain and marketing management. The course is structured as a combination of lectures, in-class case studies and group projects. All data analysis, optimization and simulation models are implemented in R (https://cran.r-project.org/ and https://www.rstudio.com/). R is a powerful, extensible and free programming language, which is gaining popularity for data scientists and business analysts. Students are expected to learn how to integrate BI with supply chain and marketing management, improve their data/analytical skills and deepen their knowledge of supply chain and marketing science from a quantitative perspective.
In the last several decades, the supply chain area has become increasingly data-driven. Traditional statistical techniques have helped supply chain planners improve operations efficiency (e.g., a better match between demand and supply via forecasting). With the growth of data accessibility in the e-commerce age and the power of new programming platforms, innovative AI methods have emerged to help supply chain managers organize/analyze data and derive actionable insights. This SCM graduate elective course will help train students who are interested in connecting AI with supply chain applications and integrating automated data processing tools with supply chain management.
The concentration and courses are offered by the Supply Chain Management Department
Sample Relevant Careers: Supply Chain Analyst, Supply Chain Planner, Inventory Analyst, Material Planner, Production Planner, Strategic Planner/Buyer, Logistics Analyst, Supply Chain Consultant, Business Intelligence Engineer, Business Intelligence Data Science Analyst, Supply Chain Manager