201B. Management Science
Organizations routinely seek to minimize costs or maximize profits, through the efficient use of resources and by effective planning and execution. Regardless of how much data a firm has, and how accurate its forecasts are, a firm nevertheless needs methods to transform predictions about the future into actionable plans and decisions. This course introduces students to prescriptive analytics and its application to management problems. The main topics are the management science core methodologies of optimization (problem formulation, solution methods, and sensitivity analysis), and simulation. We will apply these core methodologies to several functional areas of business, including operations, marketing, and finance. Effective use of management science techniques frequently saves businesses millions of dollars. The practice of management science relies heavily on computers, which use sophisticated algorithms to find optimal or near-optimal solutions to management problems. This course blends application with just the right amount of theory so that students always have a conceptual understanding of how to make good modeling decisions and to choose the right algorithm for the task at hand. We aim for students to become advanced users of optimization and simulation software, or managers with a keen eye for detail and an ability to manage technical staff implementing a management science project.
Effective use of management science techniques frequently saves businesses millions of dollars. The practice of management science relies heavily on computers, which use sophisticated algorithms to find optimal or near-optimal solutions to management problems. This course blends application with just the right amount of theory so that students always have a conceptual understanding of how to make good modeling decisions and to choose the right algorithm for the task at hand. We aim for students to become advanced users of optimization and simulation software, or managers with a keen eye for detail and an ability to manage technical staff implementing a management science project.
281. Analytical Decision-Making Models in a Digital World
This course will introduce you to prescriptive analytics and its application to management problems. Prescriptive analytics is a collection of skillsets and methodologies which are strategically important to businesses in today’s digital world. Students will learn how to identify the key aspects of real-world logistical problems, build models to quantify the effects of anticipated outcomes and suggested courses of action, use software to find optimal or near-optimal solutions, and simulate the performance of suggested policies to estimate how our decisions may unfold in the real world. Examples include optimizing the advertising mix, multi-period inventory & production planning, portfolio optimization, online advertising, and trauma care system design. The course empowers students to apply prescriptive analytics to several functional areas of business, including operations, marketing, and finance.
282. Revenue Management
Revenue Management studies how a firm should set and update pricing and product availability decisions across its selling channels to maximize profitability. It is the science of selling the right product to the right customer at the right time for the right price, and can be viewed as the demand-side complement to traditional supply-side inventory management. Enabled by digital technologies, revenue management is now pervasive across a broad range of industries. Using mathematical models and advanced analytics, students will study how airlines decide how many seats to reserve for high-paying business customers, how hotels determine when to discount their rooms, and how rental car companies determine how many reservations to overbook. Additionally, students will learn how auctions are used to price and sell online advertising, and discuss how revenue management is being used by the health care, retail, and entertainment industries.
283. Decision Analysis
Facing many important and far-reaching decision situations in your professional and personal life, this class will provide you with the digital technology tools and thought processes to approach such situations with clarity and confidence and improve your decision making skills. This course will teach the use of decision analysis digital technologies for multiple objective decisions under certainty, decision-making under risk using decision trees, fitting probability distributions to judgments or data, and Monte Carlo simulation, applied to business, government, not-for-profit, and personal decisions.
285. Supply Chain Management
This course introduces students to the tools and strategies to successfully manage uncertainty, meeting customer needs in the most timely and cost effective manner, and driving business disruptions through supply chain innovations. The use of advanced analytics and data-driven methods will be emphasized. Based on case studies, simulations, group discussions and guest lectures from practitioners, the course prepares students for managing supply chain challenges in practice such as the digital transformation, complex organizational network, globalization, and environmental and social responsibility concerns.
287. Project Management
In this digital era characterized by the storms of technology changes, software upgrades, and communication system alterations, managers need to learn to manage the non-routine tasks related to and resulting from such rapid changes. Additionally, as companies constantly devise new products and services to stay competitive, the resultant tasks do not fit into the mold of business-as-usual. Organizing such tasks into projects affords managers with the ability to meet timelines, budget, performance goals, and expectations of many dissimilar stakeholders. This course equips students with tools and techniques to effectively manage projects in a rapidly changing environment. Using a project management framework and a computer software package, students will learn about the issues, problems, and solutions to carry out a team project from initiation to termination.
288. Predictive Analytics
This course deals with predicting entities such as the demand for a product or service (commonly called forecasting) and predicting membership of known groups (commonly called classification). As such it is a blending of methodologies of forecasting and data mining. In particular we focus on multiple regression, logistic regression, neural nets, ARIMA, discriminate analysis and k-nearest neighbors. Although very technical and mathematical concepts lie behind these methodologies, our focus is more on the application of these methods to managerial problems and decision making.
In many examples, we will work with large data sets which will be split into training and validation sets in order to develop usable models. This approach facilitates model comparison with cross validation.
290. Fundamentals of Business Analytics
With data fueling the digital transformation of enterprises, Fundamentals of Business Analytics will teach concepts on how to recognize and use meaningful data. This course will focus on the business understanding, the process of business analytics, and teaching a framework to understand what information forms the key drivers that could be fed into a mathematical model. Moreover, the course emphasizes how to make use of this information to drive digital change within organizations through analytics models that propose data-driven decision-making. In addition, this course will leverage case studies involving the digital transformation in automotive, retail, healthcare, entertainment, and other select industries to showcase how the analytics framework can be used to create new markets as well as products and services.
290. Redefining Operations in the Digital World
This course will develop a process excellence driven approach for digital operations. While conventional Operations management processes leverage lean six sigma approach to excellence based on manufacturing practices, similar measures of excellence for digital operations will be necessary to minimize “defects” so that digital productivity could be defined, measured, analyzed, improved, and controlled.
Examples of digital operations in the industry where these processes are studied are in the internet and app driven consumer world, Robotic Process Automation (RPA), as well as digital manufacturing. Consumers are very demanding, and competition is fierce. To succeed in the “on-demand economy” a company needs to stand out from the crowd. Companies like Google, Amazon, Facebook, Netflix and Airbnb have developed ways of working that allow them to respond faster to consumer demands than their rivals (and are reaping the rewards). They have proven that even the largest companies can be as fleet-footed as a start-up. Case studies with these companies would be studied in this course.