MS-BA supply chain analytics track

Ensuring the foundational understanding of contextualized analytics within the business enterprise continuum by covering how data flows and is managed across the landscape of enterprise business processes.

Provides a foundation in programming fundamentals, the skills to combine and manipulate structured and unstructured data, and the ability to summarize, visualize, and draw insights from the data. This course may be waived with demonstrated Python proficiency.

Provides a solid foundation and deeper understanding of the use of quantitative modeling tools and techniques to solve problems faced in modern supply chains. Uses Excel workbooks to implement the appropriate quantitative methods, including forecasting demand, capacity planning of a manufacturing line and the line cycle time as it pertains to parts inventory management.

Addresses the use of analytics tools and techniques to enhance the ability of quality management approaches to improve processes. Introduces modern quality management approaches including six sigma and design for six sigma. Covers the define, measure, analyze, improve and control (DMAIC) improvement cycle: the core process used to drive six sigma projects. DMAIC refers to a data-driven improvement cycle used for improving, optimizing and stabilizing business processes and designs. Provides an analytics roadmap to help users work through the DMAIC problem-solving process.

Charting a roadmap for data-driven decision making and getting a practical understanding of how IT tools and techniques can allow managers to extract predictive analytics and patterns from primarily numeric data.

Explains the skills and knowledge necessary for mastery of the use of quantitative modeling tools and techniques to support a variety of business decisions. Also explores deterministic optimization techniques, including linear programming, nonlinear programming, integer programming; network models and a brief introduction to metaheuristics. Covers the use of these models for a variety of common business problems. Practical application of these models uses Excel and standalone software. Also studies how to ensure that these solutions work in a wide variety of situations (what-if analysis).

Involves formulating critical marketing problems, developing relevant testable hypotheses, collecting and analyzing data and, most importantly, drawing inferences and suggesting actionable implications.

Addresses the skills and knowledge necessary to model situations where uncertainty is an important factor. Covers models including decision trees, queuing theory, Monte Carlo simulation, discrete event simulation and stochastic optimization. Uses these models for a variety of common business problems and requires implementation of these models using Excel and standalone software. Studies how to ensure that these solutions work in a wide variety of situations (what-if analysis). Describes each of these methods in detail.

Focuses on a system designed to help students take an active role in their career development through self-reflection, skills and values assessment, market research and identifying potential roadblocks to obtaining an internship or full-time role after graduation. Also introduces the concept of a personal narrative and provides experiential learning opportunities to refine their own personal narrative and understand how to fine-tune and tailor it for a variety of career applications.

Explores supply chain management topics including environmental, project and supply chain processes. Additionally, covers processes in the areas of new product introduction development; quality control; TQM (Total Quality Mgt).

Covers theories and practices of modern logistics management within a market-driven supply chain. Begins with an outline of reasons why some operations are not efficient, then introduces management tools to improve efficiency and responsiveness followed by application of analytical tools to evaluate the current logistic management practices. Topics include logistics strategy, demand management, distribution and warehouse management, distribution/warehouse design and location decisions, transportation and delivery frequency, reverse logistics, humanitarian logistics and responsive supply chains.

Deep learning applications have become an integral part of our lives over the last decade. Alexa, Amazon Go, Waymo, Apple Face ID, and Facebook's face recognition applications are all powered by deep learning networks. Applications based on deep learning models cover a wide spectrum of industries including retail, automotive, manufacturing, health care, banking, insurance, agriculture, security and surveillance. Hands-on look at the latest models, trends and challenges of deep learning applications in business.

Uses tools and techniques to analyze unstructured data that are applied to business problems to support informed decision making and the extraction of predictive analytics and patterns from primarily nonnumeric data.

Studies management of the conversion of raw materials to finished goods including scheduling, work-in-process inventory management, and postponement/customization. Students gain a deeper understanding of the integrated supply chain of plan, source, make, deliver and return.

Applies principles, philosophies and processes of supply management to facilitate the continuous improvement and strategic design of an organization's supply management system on a global basis. Focuses on topics like performance management and analytics, project management and governance and finance. Provides a comprehensive understanding of supply management and its impact on the organization.

Covers theories and practices of modern logistics management within a market-driven supply chain. Begins with an outline of reasons why some operations are not efficient, then introduces management tools to improve efficiency and responsiveness followed by application of analytical tools to evaluate the current logistic management practices. Topics include logistics strategy, demand management, distribution and warehouse management, distribution/warehouse design and location decisions, transportation and delivery frequency, reverse logistics, humanitarian logistics and responsive supply chains.

A small class emphasizing discussion, presentations by students, and written research papers.

Preparation of a supervised applied project that is a graduation requirement in some professional majors.