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1 - 10 of 40 results for: OIT

OIT 245: Optimization and Simulation Modeling

This course provides basic skills in quantitative modeling. The emphasis is on constructing abstractions of real-world processes, and using the tools of optimization, Monte Carlo simulation and sensitivity analysis to generate and interpret recommendations. A variety of applications drawn from revenue management, healthcare, finance and manufacturing are discussed. The instructional medium used is Excel, together with appropriate packages for simulation and optimization.
Terms: Win | Units: 2
Instructors: Iancu, D. (PI)

OIT 247: Optimization and Simulation Modeling - Accelerated

The course is similar in content and emphasis to OIT 245, but is aimed at students who already have background or demonstrated aptitude for quantitative analysis, and thus are comfortable with a more rapid coverage of the topics, in more depth and breadth.
Terms: Win | Units: 2
Instructors: Bimpikis, K. (PI)

OIT 249: MSx: Data and Decisions

Data and Decisions an introductory course in probability, statistics and decision analysis. Our goal is to teach you how to evaluate quantitative information and to make sound decisions in complex situations. “D&D” combines two areas of management science: The first area, probability, provides a foundation for modeling uncertainties, such as the uncertainties faced by financial investors or insurers. We will study the mechanics of probability (manipulating some probabilities to get others) and the use of probability to make judgments about uncertain events. The second area, statistics, provides techniques for interpreting data, such as the data a marketing department might have on consumer purchases. Statistical methods permit managers to use small amounts of information (such as the number of people switching from Verizon to AT&T in an iPhone test marketing program) to answer larger questions (what would AT&T’s new market share be if the iPhone is launched nationally?)
Terms: Sum | Units: 2
Instructors: Hasan, S. (PI)

OIT 256: Electronic Business (Accelerated)

This course focuses on the intersection of strategy and information technology. It considers how you can take advantage of new technology opportunities and how they change the structure of firms, industries and value chains, with an emphasis on business issues. Classes combine lecture and case study discussions and the workload is above the GSB average.
Terms: Win | Units: 3

OIT 258: Incentive Mechanisms for Societal Networks

In many of the challenges faced by the modern world, from overcrowded road networks to overstretched healthcare systems, large benefits for society come about from small changes by very many individuals. This course survey the problems and the cost they impose on society. It describes a series of pilot projects which aim to develop principles for inducing small changes in behavior in Societal Networks--transportation networks, wellness programs, recycling systems and, if time permits, energy grids. Students will learn how low-cost sensing and networking technology can be used for sensing individual behavior, and how incentives and social norming can be used to influence the behavior. The effectiveness of this approach in pilots conducted in Bangalore (commuting), Singapore (public transit system), Stanford (congestion and parking), and a wellness program at Accenture-USA will be discussed. Students may experience the incentive platform as participants.
Terms: Win | Units: 2

OIT 262: Operations

This course focuses on basic managerial issues arising in the operations of both manufacturing and service industries. The objectives of the course are to familiarize students with the problems and issues confronting operations managers and to introduce language, conceptual models, and analytical techniques that are broadly applicable in confronting such problems. The spectrum of different process types used to provide goods and services is developed and then examined through methods of process analysis and design.
Terms: Spr | Units: 3

OIT 265: Data and Decisions

This is the base version of D&D. This course introduces the fundamental concepts and techniques for analyzing risk and formulating sound decisions in uncertain environments. Approximately half of the course focuses on probability and its application. The remainder of the course examines statistical methods for interpreting and analyzing data including sampling concepts, regression analysis, and hypothesis testing. Applications include inventory management, demand analysis, portfolio analysis, surveys and opinion polls, A/B testing, environmental contamination, online advertising and the role of analytics in business settings more generally. The course emphasizes analytical techniques and concepts that are broadly applicable to business problems.
Terms: Win, Spr | Units: 4

OIT 267: Data and Decisions - Accelerated

Data and Decisions - Accelerated is a first-year MBA course in probability, statistics, multiple regression analysis, and decision trees for students with strong quantitative backgrounds. Probability provides the foundation for modeling uncertainties. Statistics provides techniques for interpreting data, permitting managers to use small amounts of information to answer larger questions. Regression analysis provides a method for determining the relationship between a dependent variable and predictor variables. Decision tree analysis consists of quantitative approaches to decision making under uncertainty. Students taking this course need to be comfortable with mathematical notation, algebra, and some calculus. If you are not confident with your quantitative abilities, then you should enroll in OIT 265. Accelerated D&D will cover material covered in OIT 265 faster and in more depth. One main difference is that Accelerated D&D will cover the additional topics of advanced multiple regression analysis (e.g., correction for autocorrelation), discrete dependent variable models, and panel data. A multiple regression group project is required.
Terms: Win | Units: 4

OIT 268: Making Data Relevant

Data is everywhere. Firms collect it. Data on customers' preferences are collected through websites or loyalty programs or cash registers. Data on employees' traits are collected through in-house databanks or social networking sites. All of us are used to thinking about data. How can you make data relevant to doing your job? How can data analysis serve to increase your competitive advantage over that of others? This class goes beyond graphing data in bar charts or time trends. It makes you think about causal relationships. The examples we use are primarily taken from talent management, because it's easy to think about our own careers or those of our employees. But the tools covered extend to all contexts, and your project is on an idea of your choosing. The class focuses on the use of regressions to think experimentally. To take the class, you should have covered regression analysis in a former class (such as an econometrics course for economics majors) or be comfortable with learning basic math concepts quickly. You also should understand distributions of data (such as the Bell curve, or normal distribution), but this topic is not covered. There are no required proofs or derivations; you've done that as undergraduates. This is about using data: we use cases, examples, Notes written for the class, and a quiz, final exam, and several assignments in which you play with data sets to answer questions. Note that this 4-unit course, if successfully completed, counts for the Data Analysis foundations requirement.
Terms: Spr | Units: 4
Instructors: Shaw, K. (PI)

OIT 269: Sloan: Operations

This course focuses on basic managerial issues arising in the operations of both manufacturing and service industries. The objectives of the course are to familiarize students with the problems and issues confronting operations managers and to introduce language, conceptual models, and analytical techniques that are broadly applicable in confronting such problems. The spectrum of different process types used to provide goods and services is developed and then examined through methods of process analysis and design.
Last offered: Autumn 2011
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