Partnering to Advance STEM Leaders in Africa

Today’s business computing infrastructures are producing the large volumes of data organizations need to make better plans and decisions. This course provides an introduction to the processes, technologies, and techniques for organizing, analyzing, visualizing, and interpreting data and information about business operations in a way that creates business value. During the course, students will study a variety of business decisions that can be improved by analyzing data about customers, sales, and operations, preparing students to be knowledgeable producers and consumers of business intelligence. Students will apply commercially available business intelligence software to develop performance dashboards to facilitate organizational decision-making. The course explores the technical challenges of organizing, analyzing, and presenting data and the managerial challenges of creating and deploying business intelligence expertise in organizations. The course includes business cases, in-class discussion, and hands-on analyses of business data. It is designed for any student interested in learning about data-driven business performance management and decision-making, including students whose primary focus is Data Science, IT, Marketing, Operations, or Business Management.

The operations function in an organization is focused on the transformation processes used to produce goods or provide services. Operations design is driven by strategic values, and innovative improvements can support sustained competitive advantage. In this course, a variety of analytical and statistical techniques are introduced to develop a deep understanding of process behavior, and to use this analysis to inform process and operational designs. Topics such as process analysis and value stream mapping, postponement and global and local supply chain strategies, queuing models, and managing system constraints are covered using case studies and hands-on activities such as on-line simulations. Non-traditional operations systems are also explored. The skills required to model an operational system, to reduce variation and mitigate bottlenecks, to effectively present resource needs, and to adjust capacity and inventory service levels are practiced during the course. (Students cannot get credit for OIE 501 and OIE 500)

This course develops the skills business students need for handling data. It focuses on student skills in (1) cleaning and preparing data for analysis, (2) writing SQL queries to access and manipulate data, and (3) ethical uses of data and data privacy issues. It also covers the types of data typically found in organizations, e.g., employee, customer, product, marketing, operations, and financial data.

Risk management deals with decision making under uncertainty. It is interdisciplinary, drawing upon management science and managerial decision-making, along with material from negotiation and cognitive psychology. Classic methods from decision analysis are first covered and then applied, from the perspective of business process improvement, to a broad set of applications in operations risk management and design including: quality assurance, supply chains, information security, fire protection engineering, environmental management, projects and new products. A course project is required (and chosen by the student according to his/her interest) to develop skills in integrating subjective and objective information in modeling and evaluating risk. (Prerequisite: OIE 501 or equivalent content, or instructor consent.) (Students cannot get credit for both OIE 542 and OIE 541.)

In this course, Lean Six Sigma is presented as an organizational improvement system and a set of process analysis and statistical tools that have helped the world’s leading organizations achieve operational excellence, saving millions of dollars and improving customer satisfaction. This course is organized in three parts: part one covers the essentials of Lean Six Sigma, including fundamental concepts and problem-solving methods; part two of the course covers Lean Six Sigma tools, including topics such as value-stream mapping, process capability, and experimental design; part three describes the major activities in a Lean Six Sigma roadmap, from identifying core processes to executing improvement projects to sustaining Lean Six Sigma gains.

How do we navigate complex human systems in organizations? How do we foster innovation within organizations? In this course, we explore the paradoxes, opportunities, and hidden systemic challenges that arise on teams and projects, and in working across networks and within innovative organizations. Students will learn to more deftly manage the inherent challenges and opportunities of cross-cultural and multi-disciplinary teams; work through or avoid dysfunctional team and organizational conflicts; wrestle with ambiguity and uncertainty; negotiate change by learning to work with networks of power and influence; and analyze the individual, group, organizational and contextual dynamics that enable and constrain productive and innovative work in organizations. (Students cannot get credit for OBC 505 and OBC 500)

This course covers mathematical optimization beyond the foundational concepts of linear programming. Approaching from the perspective of obtaining globally optimal solutions, a variety of optimization problem classes will be addressed, likely including integer programming, nonlinear programming, and stochastic programming. While ensuring an appropriate level of theory, the emphasis of the course will be on the mathematical modeling and computational solution aspects of such problems that may arise in the finance, healthcare, humanitarian, inventory, nonprofit, operations, production, staffing, and supply chain sectors, among others. (Prerequisite:  OIE 552, equivalent knowledge about optimization and linear programming, or consent of the instructor).

This course is designed to provide students with a variety of quantitative tools and techniques useful in modeling, evaluating and optimizing operation processes. Students are oriented toward the creation and use of spreadsheet models to support decision-making in industry and business.

This course is designed to equip students with research methods and tools that are used for marketing decision making. Students will learn to conduct, use, apply, interpret, and present marketing research in order to become effective decision makers. The topics covered in this course include problem formulation, research design, data collection methods, data analysis, and finally presentation of a research plan. This course will be an activity-based course involving design, implementation, and presentation of a marketing research plan. Basic knowledge of marketing and statistical concepts is assumed.

This course explores how Machine Learning (ML) and Artificial Intelligence (AI) is applied to solve business problems, to satisfy specific business needs, or to discover new opportunities for businesses. Applications of ML and AI are constantly evolving across many industries. This course utilizes existing AutoML solutions to address issues identified in business case studies (e.g. predicting hospital readmissions, loans likely to default, customer churn). The course covers the machine learning project life cycle starting with defining ML project objectives, acquiring and exploring data, modeling using AutoML tools, interpretation of models and communication of outcomes, and implementation and deployment of predictive models in organizations.

This course is the capstone course for the STEM-based, specialty MS programs in the Foisie School of Business. This course serves as a practical integration of the STEM-based tools, techniques, and skills and the related business theories and practices that students learned in their MS program. The medium is a major team-based project in the form of an actual corporate STEM-based business need for which students will develop solutions. Students will produce a written report that documents and provides the financial, organizational, and technical rationale for the solutions. They will also formally present their results to the project sponsors. Students are expected to have completed (or are currently completing) all the courses requirements for their MS program prior to taking the capstone project. While the capstone requirements are the same for all STEM-based, specialty MS programs in the Foisie School of Business, the actual content of the project will differ by sponsor and by MS program. Students must take the appropriate section of BUS 596 for each MS program they complete.