1 - 10 of 34 results for: OIT

This course satisfies the Management Foundations requirement in Modeling for Optimization and Decision Support (MODS). The course provides basic skills in quantitative modeling, using Excel as the instructional medium. Particularly, the course teaches model building, optimization, and Monte Carlo simulation. The emphasis is on model formulation and the interpretation of results.

This is the only Accelerated option offered in 2010-11 to satisfy the Management Foundations requirement in Modeling for Optimization and Decision Support (MODS). It is a modified version of OIT 542 (the Advanced Applications option), and the description of that course applies except as follows: In OIT 247, more class time will be devoted to development of modeling skills related to optimization, and only about 80% as much material will be covered on pricing and revenue management.nnnThis course is appropriate for students who are comfortable with Excel, have reasonably good math training and some modeling experience, and are interested in pricing and revenue management. No prior exposure to optimization modeling is assumed.

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.

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.

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.

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.

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.

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.

This course satisfies the Management Foundations requirement in Modeling for Optimization and Decision Support (MODS), and is the primary core course for the joint MBA - MS in Environment and Resources. For students who lack an undergraduate degree in science or engineering, OIT 338 is challenging but doable; it does not assume knowledge of environmental science or proficiency in quantitative analysis beyond admission requirements for the MBA program. Students will learn the fundamental science of ecosystems, climate and energy systems, by building decision-support models for managing these systems. In so doing, students will develop widely-applicable skills in model representation in a spreadsheet, optimization, and Monte Carlo simulation.