Operations Management (716)

53:716:502 Business Analytics (3) Analytic competency is becoming tremendously important in the business world and is often the factor that distinguishes leading firms in any industry. This course is intended to provide an introductory overview of how firms implement data-driven decision-making. Students will learn statistical concepts, use spreadsheet modeling, and learn through a mix of lectures, cases, and class discussions. Students are required to have a functioning computer with Microsoft Excel installed. Within Excel, you must have DATA ANALYSIS and SOLVER functionality. The course's primary goal is to coach students on fact-based decision-making and enable them to carefully plan and run business experiments to make informed managerial decisions.

53:716:504 Management Science (3) This course introduces students to the application of quantitative models and their related mathematical techniques, to solve real-world business problems. A blend of analysis and synthesis is emphasized to generate meaningful solutions for managers. Topics include single- and multiple-criteria decision methods, project scheduling, transportation and assignment problems, queuing theory, and decision analysis.

53:716:513 Operations and Supply Chain Management (3) This course aims to (1) familiarize students with the major operational issues confronting managers, and (2) provide students with concepts, insights, and tools to deal with these issues. Topics include inventory management, capacity planning, forecasting, quality management, lean systems, supply chain management, and logistics. Previous title: Operations Management Productivity and Quality.

53:716:516 Total Quality Management (3) This course provides the development, practice, and processes of quality management. It focuses on increasing productivity through continuous improvements in quality. Case studies and role-playing exercises are used in the instruction. Prerequisite: 53:716:513.

53:716:521 Directed Study in Operations Management (3,3) Supervised by an individual faculty member and approved by the associate dean of graduate studies. Prerequisite: As determined by instructor.

53:716:531 Supply Chain Management (3) This course provides the understanding of how supply chain design and planning decisions impact the performance of the firm as well as the entire supply chain. It links supply chain structures and logistical capabilities in a firm and utilizes the concepts learned in various functional areas such as management, marketing, and finance within the context of supply chain management. A blend of lectures and case studies are employed to facilitate learning of course materials. Prerequisite: 53:716:513.

53:716:535 Big Data Analytics (3) This course provides students with an in-depth introduction to the (big data management platforms) Hadoop ecosystems, which is an environment used by companies to store and manipulate "Big Data" of a size and scale that cannot be handled by traditional databases. The course also provides exposure to state-of-the-art data mining algorithms for clustering, classification, and collaborative filtering. Note: Students should have familiarity with the Python programming language before coming into this course. Prerequisite: 53:716:502

53:716:540 Social Media and Sentiment Analysis (3) This course enables students to ingest Big Data from APIs for social media platforms such as Twitter. After assembling data from social media, students learn to analyze the data to gain business insights. Concepts for the analysis of social media, such as community detection and assignment, node centrality, information diffusion, and opinion formation will be presented. Students will also learn the process for sentiment extraction, opinion mining, and recognizing opinion spam. Prerequisites: 53:716:502 and 53:623:517. Note: Students should have familiarity with the Python programming language before coming into this course.

53:716:545 Machine Learning Application (3) The focus of the course will be to introduce basic concepts in machine learning and data-analytic thinking to students, with an applied business orientation. Students will understand how to use data to competitive advantage and to build and evaluate models for decision-making. Companies today have access to vast amounts of data from their business operations. Data science is the craft of extracting patterns from this data and using available information for competitive advantage. This course represents an introduction to data science and data analytic thinking. Students will learn to leverage data to answer business questions relating to classification tasks (e.g., will this credit card prospect default or not?). Prerequisites: Familiarity with Python programming language as well as a working knowledge of Jupyter notebooks.

53:716:550 Supply Chain Analytics (3) This course illustrates how the field of data analytics can be applied to optimally manage supply chains. Students learn to apply data driven decision-making methodology to the field of supply chain management. Topics will encompass all portions of a supply chain including sourcing, procuring, buying, making, moving, and selling. Topics will include designing and planning supply chains, transportation analysis, facility and warehouse location models, demand and inventory management, and supply chain risk analytics. Case studies and hands-on assignments will introduce students to current business applications and innovative use of these ideas. Prerequisite: 53:716:502.

53:716:671 Special Topics in Operations Management (3) Designed to integrate course materials, introduce newer philosophies and techniques in operations management, and apply them to selected problems. Topics vary from semester to semester. Prerequisite: As determined by faculty.