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 course satisfies the Management Foundations requirement in Modeling for Optimization and Decision support (MODS). It is aimed at students who already have background or demonstrated aptitude for quantitative analysis, and thus are comfortable with more rapid coverage of the required MODS topics: (1) modeling in a spreadsheet environment, (2) optimization modeling, and (3) Monte Carlo simulation. No prior course work on these topics is expected or required. However, in contrast to OIT 245, the topics will be covered at a faster pace and with less reliance on introductory tutorials and laboratory sessions to learn the concepts. This allows time for coverage of a few additional topics that expand students' appreciation for modeling, optimization and simulation. An example additional topic is discrete-event simulation of systems subject to congestion and delay. The emphasis is on model formulation, and analysis and interpretation of the results. The applications covered will draw from several areas including operations, finance and marketing. Examples include production and capacity planning, investment management, and portfolio optimization. OIT 247 is a two-unit course, with 9 required class sessions, one optional session, and a final exam. Students with undergraduate degrees in math, science or engineering, and students who have accumulated substantial modeling experience in their work life, particularly with simulation or optimization, should take OIT 247 in preference to OIT 245.

This course covers the fundamental issues pertaining to the management of Information Technology for general managers. The course is structured around the three main phases of decision making for IT management: selection, implementation and value derivation. These three stages of managerial decision making will be studied around IT applications that impact a single firm as well as those span the boundaries of firms or emerge in network environments. Some examples of topics studied are process and software selection, enterprise technology implementation, and utilization of network technology tools for building strategic advantage. Through case-based learning, the course aims to give students decision making skills for management of technology and generating competitive advantage by successful implementation and efficient utilization of IT.nnnFor students enrolled in OIT 258 during the Autumn Quarter 2009-10 the final will be scheduled in the afternoon on Tuesday, December 8, 2009 and must be taken at the GSB. Alternate exam times will not be scheduled. If you anticipate a personal time conflict with this exam date, you should not enroll in OIT 258 in the autumn.

This course is intended to provide students who do not have significant background in technology with an appreciation for managerial issues related to Informational Technology (IT) within a typical firm, not necessarily in the technology sector. The course discusses how value is delivered by IT solutions, what must be done by a firm to realize this value, and whether an advantage bestowed by technology is sustainable. On the technical side, students are introduced to complexity, reliability, security, and scalability via some common IT solutions. On the organizational side, risks, costs of adoption, difficulties with implementation, and decision architectures enabled by IT are studied. The course is not intended to be a technical primer, although it does touch upon several current technologies.

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 focuses on the business processes through which real work is accomplished, such as product development, order fulfillment, and customer service. We will discuss fundamental concepts embodied in the total quality, time-based competition, business process reengineering, and lean manufacturing movements. Specific topics include: capacity management, the impact of variability on process performance, project management techniques, and dynamic flow management (priority scheduling, triage, multi-tasking). Cases and exercises are drawn from a variety of industries, including services (e.g., back-room operations in financial services), design, manufacturing, and health care. Class members should be comfortable with modeling techniques. About one-third of the material is related to tools of process analysis, including several computer assignments involving simulation software.

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), two-group discriminant analysis, chi-square analysis, and stratified random sampling. A multiple regression group project is required.

This course introduces modeling and managerial uses of computers. Three major themes are developed: (1) the building, using, and interpretation of computer-based models which aid managers in making decisions, (2) the analysis and interpretation of empirical data for use in computer-based models, and (3) the implementation of organization-wide systems combining technology, data, and models.

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/EDEA/Extreme Affordability.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 professional degree programs that combine the MBA, JD or MD with the 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. Lecture is a more common mode of instruction in OIT 338 than in most other GSB classes. OIT 338 is a 4-unit course, with 20 class sessions.

Fundamental science of ecosystems, climate and energy. Spreadsheet modeling, optimization, and Monte Carlo simulation applied to resource management and environmental policy. Similar to OIT 338, but allocates more class time to environmental/energy science and implications for management and policy, and less class time to fundamentals of modeling/optimization/simulation. Space permitting and with instructor's permission, non-GSB students with knowledge of modeling/optimization/simulation may take the course.

This course is about the intersection of business, electronic commerce and information technology, with an emphasis on strategy and business issues. It focuses on ways you can take advantage of new technology opportunities and how they change the structure of firms, industries and value chains. For a typical class, you will prepare an in-depth case study, and the class discussion will start from the business problems presented by the case study, how one might address them, what is the role and impact of the enabling technologies, and what are some general lessons one can draw beyond the problems presented by the case. nnnTo give you a bit of a flavor, consider one class that will examine how Apple is transforming the music industry. You will read a case study that summarizes recent developments in online music delivery, with an emphasis on Apple's iPod and the iTunes Music Store. The enabling technologies are important, but you will learn about them from the perspective of the associated strategy and business issues. The class will analyze the changing structure of the IT industry; how Apple and music fit within these changes; how Apple is actually making money; what is expected to happen in the future; the strategies followed by some key players in this market -- Apple, Microsoft, Napster (in its legal reincarnation), RealNetworks, and device manufacturers like Creative, Dell, HP etc.; what are the implications of new technologies for this market; and the interplay between software, hardware and content. Following the class discussion, you should have a much deeper understanding of this market and how new technology is shaping it than one could glean from newspaper articles or shallow analyses. But more importantly, you will learn some general lessons that apply to many different business situations.

Electronics, computing, networks and software applications have become an integral part of business. The course is aimed at the student who wishes to learn those electronic and computer science concepts needed to understand how computers, networks, and the software that runs them operate, but who lacks background in engineering or computer science. The premise of the course is that adequate knowledge of technology is now a prerequisite for a successful manager, but that knowledge does not have to be at the level of rigor required in the practice of engineering or computer science. This course is intended to provide a basic literacy in these areas, with an emphasis on implications for managers and organizations. A meaningful course that focuses on particular technologies is difficult because rapid changes in any technology can quickly render today's lessons obsolete. Therefore, this course will stress fundamentals and trends, rather than a snapshot of the current status of different technologies. As a result, classroom coverage of current "hot" topics in technology is subordinate to giving the technology concepts necessary for one to learn such current (and future) topics on their own. Investigation of technology will be facilitated by lectures readings and homework assignments. Students will have an opportunity to investigate and learn more about a particular technology in more depth as part of a term project. nnnThe general flow of the course will focus upon four areas approximately as follows: Part I Electronic Systems: Fundamental Electronics (2 sessions), Digital and Microelectronics (2 sessions), Computer Hardware and Systems (2 sessions), Technology Trends (1 session), Communications including wireless (2 sessions); Part II Networks: Networked Computing (1 session), the Internet (2 sessions); Part III Software: Software and software development (3 sessions), Data Base Technology (1 session); and Part IV The Web: Clients and Servers (1 session), Case study of a web site (1 session), Video/Multimedia (1 session). nnnThe course is specifically designed for students with liberal arts or soft science backgrounds who have career ambitions in high-tech or who wish to be more technically aware as managers. Students with hard science, engineering or computer science backgrounds are welcome but must avoid redirecting the class discussion into narrow or advanced material that causes dysfunction to less technical colleagues. Students may elect either to take a final exam or do a term project. Students electing to do a term project will create it as a Web page. Separate training for building a Web page will be offered.

Globalization of businesses has resulted in companies having to manage global networks of suppliers, integrators, contract manufacturers, logistics service providers, distributors, and service support operators in geographically dispersed locations. The customer network is also globally distributed. This course will focus on (1) how global and international companies can overcome the geographical, cultural, and organizational barriers, and leverage the strengths of the network to create values, and (2) how these companies may use different ways to manage operations in different regions to take full advantage of the local strengths and limitations. The course will be based on cases, mostly developed in the last two years, on innovative strategies and tactics used by global and international companies.

Need approval from sponsoring faculty member and GSB Registrar.

This is the Advanced Application option in the menu of courses that satisfy the Management Foundations requirement in Modeling for Optimization and Decision Support (MODS). Three core modeling topics are covered in rapid-review fashion - model representation in a spreadsheet environment, optimization theory, and stochastic models - but primary emphasis is on the application domain described immediately below. OIT 542 is a two-unit course, with nine class sessions plus a final exam. nnnSystems for price and revenue optimization - also called yield management, dynamic pricing, or revenue management - combine the use of information technology, statistical forecasting, and mathematical optimization to make tactical decisions about pricing and product availability. A familiar example is the passenger airline industry, where a carrier may sell seats on the same flight at many different fares, with fare availability changing as time advances and uncommitted capacity declines. Over the last 25-30 years, revenue optimization practices have transformed the transportation and hospitality industries, where fixed capacity and advance reservations by customers are important structural factors. But model-based, data-driven pricing systems are increasingly common in other industries that have different structures, such as financial services and retail clothing.nnnIn this course students learn about the model structures and modelling techniques that underlie systems for price and revenue optimization. Two topics are given roughly equal emphasis: model-based tactical pricing, including customized pricing and retail markdown management; and classical revenue management, where automated logic is used for booking control (that is, to make yes-or-no decisions in response to booking requests from customers), rather than to set prices explicitly.nnnOIT 542 is tailored to students who already have command of basic modelling techniques and wish to learn about their application in an important business context. To be specific, a prior college course on optimization modelling is assumed as background. (Typically, such courses focus on linear programming, or linear optimization, with secondary coverage of non-linear programming and discrete optimization.) Various aspects of optimization theory will be covered in quick-review format, along with the basics of spreadsheet model representation and stochastic modelling, in order to standardize terminology and establish certain conventions that facilitate grading. In exceptional cases, for students who have strong math background and high mathematical aptitude but no prior coursework on optimization, the background knowledge assumed in OIT 542 may be acquired through self-study; appropriate study materials will be suggested by the instructor upon request. The course is entirely appropriate for second-year MBA students who have completed either OIT 245 or OIT 247 as their MODS menu selections. nnnOIT 542 draws heavily on knowledge acquired and skills developed in two other GSB foundational areas: Data and Decisions (OIT 265) and Microeconomics (MGTECON 200 or 203); it is positioned in the spring quarter so that first-year students can complete those courses beforehand. Students are required to construct and analyze at least one model for every class session, to make in-class presentations on some of those models, and to hand in four of them for grading.

This course is a Bass Seminar. This course covers a variety of topics in homeland security: bioterrorism (attacks with contagious agents such as smallpox or non-contagious agents such as anthrax, and attacks on the food supply), pandemic influenza, nuclear security at ports and around cities, the biometric aspects of the US-VISIT Program, the intersection of homeland security and immigration, and suicide bombings. For each of these topics, students will typically read one academic paper that focuses on the operations aspects of the problem, and one reading about the strategic aspects of the problem. For each topic, the professor will spend part of the class lecturing on the problem (including how the results of the academic paper were implemented), and a student will be assigned as a discussant (in addition to a classwide discussion).

Biodesign Innovation is a two-quarter sequence of four two-unit courses in which students learn how to develop comprehensive solutions (most commonly medical devices) to some of the most significant medical problems. The course has a core component (OIT 581 for Winter Quarter and OIT 583 for Spring Quarter) and an experiential project component (OIT582 for Winter Quarter and OIT 584 for Spring Quarter). In the core component, students learn the basic principles of biodesign innovation: methods of validating medical needs; techniques for analyzing intellectual property; basics of regulatory (FDA) and reimbursement planning; early market analysis; design principles; brainstorming and early prototyping. Course format includes expert guest lecturers and faculty-led practical demonstrations. Students may take the core component alone or in combination with the project component OIT582, OIT584. Students who only take the core component (OIT 581) will serve as "commercialization and marketing consultants" to multidisciplinary teams of students in the project course (OIT 582).

Students work in multidisciplinary teams at the intersection of medicine, engineering and business to develop a comprehensive solution to an important medical need of their choice. With coaching from faculty and real-world experts, the teams identify an important medical need and through brainstorming they develop several potential conceptual approaches to solving the need and pursue initial prototyping, along with planning for regulatory and reimbursement pathways. The project culminates with a presentation to a panel of venture investors and entrepreneurs. In previous years, student teams examine needs in emergency and acute care, orthopedics, cardiovascular, wound closure etc. Concurrent registration at OIT581 is required. An application needs to be submitted online.

Two quarter sequence (continuation of OIT581 -- see OIT 581 for a general description of the Biodesign Innovation courses). The second quarter focuses on how to take a conceptual solution to an important medical need forward from early concept to technology translation, development and possible commercialization. Students expand on the topics they learned in OIT581 to learn about prototyping; patent strategies; advanced planning for reimbursement and FDA approval; choosing translation and commercialization route (licensing vs. start-up); marketing, sales and distribution strategies; ethical issues including conflict of interest; fundraising approaches and cash requirements; financial modeling; essentials of writing a business or research plan; strategies for assembling a development team. Students who take OIT583 but not the project-component (OIT584) will sever as a "commercialization consultant" to a team in OIT584. OIT581 is a pre-requisite.

Students work in multidisciplinary teams at the intersection of medicine, engineering and business to further develop and refine the solutions they identified in OIT 582. The focus this quarter is on implementation. The teams select the most promising solution from the concepts of the first term and move forward into prototyping and project planning. Teams develop specific strategies for patenting, FDA submission, commercialization and third-party reimbursement, along with funding strategies (e.g. licensing agreement or launching a start-up). The project culminates with a presentation to a panel of venture investors. OIT 583 should be taken concurrently. Permission of instructor needed if student has not taken OIT 582.

The goal of this course is to provide first-year Ph.D. students in OIT with sufficient fundamentals to subsequently take advanced research seminars. The course covers the very basics of six topics: queueing theory, inventory theory, multi-echelon inventory theory, game theory, stochastic dynamic programming and econometrics. Lectures will be given by advanced Ph.D. students in OIT.

In tandem with OIT 603, this course explores the application of stochastic modeling and optimization to two closely related problem areas: (a) dynamic price selection, and (b) dynamic allocation of limited capacity to competing demands. As background, students are assumed to know stochastic process theory at the level of Statistics 217-218, microeconomics at the level of Economics 202N, and optimization theory at the level of MS&E 211, and to have some familiarity with the basic ideas of dynamic programming. Additional dynamic programming theory will be developed as needed for the applications covered. Emphasis will be on current research topics, especially in the realm of airline revenue management.

In tandem with OIT 602, this course explores the application of stochastic modeling and optimization to two closely related problem areas: (a) dynamic price selection, and (b) dynamic allocation of limited capacity to competing demands. As background, students are assumed to know stochastic process theory at the level of Statistics 217-218, microeconomics at the level of Economics 202N, and optimization theory at the level of MS&E 211, and to have some familiarity with the basic ideas of dynamic programming. Additional dynamic programming theory will be developed as needed for the applications covered. Emphasis will be on current research topics, especially involving customized pricing of financial services. OIT 602 is not a prerequisite for OIT 603 but is highly recommended.

This course provides an overview of research in supply chain management (SCM). It has three parts. The first part reviews basic tools of SCM research through selected readings in economics, IT and operations research. The second part reviews the literature in SCM, covering topics such as inventory models, information sharing, information distortion, contract design, value of integration, performance measurement, risk management, and the use of markets for procurement. The last part is devoted to recent advances in SCM research.

This course covers basic analytical tools and methods that can be used in research in operations and information systems. The emphasis is on foundations of stochastic inventory theory. Basic topics include convexity, duality, induced preference theory, and structured probability distributions. Much of the course is devoted to Markov decision processes, covering finite and infinite horizon models, proving the optimality of simple policies, bounds and computations, and myopic policies.

This seminar will highlight the research advances on the use of information technology in supply chain management. Such usage has helped companies sharing information to coordinate their supply chain and to realign their incentives. It has also helped reduce the so-called bullwhip effect. Latest information technology like RFID (radio-frequency identification) has also enabled visibility and structural changes that result in significant supply chain performance enhancements. This seminar will focus on the modeling approaches used by researchers that tried to capture the values and potentials of such applications.

This course is offered for students requiring specialized training in an area not covered by existing courses. To register, a student must obtain permission from the faculty member who is willing to supervise the reading.

This course is elected as soon as a student is ready to begin research for the dissertation, usually shortly after admission to candidacy. To register, a student must obtain permission from the faculty member who is willing to supervise the research.

Firms in many industries are scrambling to develop innovative ways to move products from raw materials through manufacturing to customers more quickly and efficiently. Some are responding by necessity to competition, both domestically and internationally. Others are capitalizing on the continuing stream of dramatic improvements in information technology. They redesign their supply chains to gather, process, transmit, share, and exploit vast amounts of information quickly and cheaply. Still others are applying the radically different philosophy of seeking a cooperative approach among all the players in the supply chain. Huge improvements have been enjoyed by firms able to optimize over their entire supply chains and figure out how to share the resulting gains while breaking down the traditional adversarial relationships. Some redesign their chains to bypass unneeded stages. Other innovations derive from deregulation and lower tariffs. This course examines many of the recent innovations in this area.

Processing network models may be used to represent service delivery systems, multi-stage manufacturing processes, or data processing networks. The first half of this two-unit course consists of lectures on performance analysis (e.g., estimating congestion and delay) for classical product-form networks and for Brownian networks. The second half consists of student presentations of recent papers on managing processing networks, typically with game-theoretic aspects. Prerequisites: Statistics 217 and 218, or consent of instructor; some prior exposure to stochastic models in general, and queueing theory in particular, is useful but not essential.