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1 - 10 of 36 results for: OIT

OIT 245: Modeling for Quantitative Analysis

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: Bimpikis, K. (PI)

OIT 247: Modeling for Quantitative Analysis - 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: Iancu, D. (PI)

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: 4

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 theory and decision analysis, including decision trees, decision criteria, the value of information, and simulation techniques. 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, lotteries and gambling, portfolio analysis, insurance, auctions, surveys and opinion polls, environmental contamination, failure analysis and quality control. 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, Spr | 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.
Terms: Aut | Units: 4

OIT 333: Entrepreneurial Design for Extreme Affordability

This course is a Bass Seminar. Project course jointly offered by School of Engineering and Graduate School of Business. Students apply engineering and business skills to design product prototypes, distribution systems, and business plans for entrepreneurial ventures in developing countries for challenges faced by the world's poor. Topics include user empathy, appropriate technology design, rapid prototype engineering and testing, social technology entrepreneurship, business modeling, and project management. Weekly design reviews; final course presentation. Industry and adviser interaction. Limited enrollment via application; see http://extreme.stanford.edu/index.html for details.
Terms: Win | Units: 4

OIT 334: Entrepreneurial Design for Extreme Affordability

This course is a Bass Seminar. Project course jointly offered by School of Engineering and Graduate School of Business. Students apply engineering and business skills to design product prototypes, distribution systems, and business plans for entrepreneurial ventures in developing countries for challenges faced by the world's poor. Topics include user empathy, appropriate technology design, rapid prototype engineering and testing, social technology entrepreneurship, business modeling, and project management. Weekly design reviews; final course presentation. Industry and adviser interaction. Limited enrollment via application; see http://extreme.stanford.edu/index.html for details.
Terms: Spr | Units: 4

OIT 343: D-Lab: Design for Service Innovation

Students in multidisciplinary teams work with a partner organization to design new services that address the needs of an underserved population of users. Teams identify an unmet customer needs, develop and prototype new service designs (e.g. web services, services with a product component, educational campaigns), test these services with real customers and develop an implementation plan. Fundraising strategies are also explored and tested. We will offer two sections: financial services (MW: 1:15 pm - 3:00 pm); health services (MW: 4:15 pm - 6:00 pm). The specific domains for the two sections will be announced in the fall based on the needs of partner organizations. Possible domains for financial services: financial literacy for young adults, planning for major expenses at retirement, financial services for the underserved. For health services: transition to adulthood of pediatric patients with chronic conditions, transitions to nursing care for elderly patients. See http://designforservice.stanford.edu/.
Terms: Win | Units: 4
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