Results for OIT

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.

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.

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?)

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.

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.

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 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.

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.

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.

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 or service prototypes, distribution systems, and business plans for entrepreneurial ventures that meet that 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 or service prototypes, distribution systems, and business plans for entrepreneurial ventures that meet that 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.

Design for service innovation is an experiential course in which students work in multidisciplinary teams to design new services (including but not limited to web services) that will address the needs of an underserved population of users. Through a small number of lectures and guided exercises, but mostly in the context of specific team projects, students will learn to identify the key needs of the target population and to design services that address these needs. Our projects this year will focus on services for young adult survivors of severe childhood diseases. For the first time ever, children who have cystic fibrosis, rheumatoid arthritis, major cardiac repairs, organ transplants, genetic metabolic disorders, and several forms of cancer are surviving. The first wave of these survivors is reaching young adulthood (ages 18-25). Many aspects of the young adult world are not yet user-friendly for them: applying to and then entering college, adherence to required medication and diet, prospects for marriage and parenthood, participation in high school or college sports, driving, drinking, drugs, and more. Our aspiration is to develop services to improve these young adults? options for a fulfilling and satisfying life. The course is open to graduate students from all schools and departments: business (MBA1, MBA2, PhD, Sloan), Medicine (medical students, residents, fellows and postdocs), engineering (MS and PhD), humanities, sociology, psychology, education, and law. Students can find out more about this course at: http://DesignForService.stanford.edu; GSB Winter Elective BBL Jan 10th, 12 noon - 1 pm; D-School Course Exposition Feb 3rd, time TBA. Admission into the course by application only. Applications will be available at http://DesignForService.stanford.edu on Jan 13th. Applications must be submitted by Feb 4th midnight. Students will be notified about acceptance to the course by Feb 7th . Accepted students will need to reserve their slot in the course by completing an online privacy training course. Details about online training will be provide to accepted students. The training is related to the protection of our partners' privacy. Application Deadline: Noon, Feb 4th.

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. Case studies include Salesforce.com, Apple, Google, Netflix, Linden Lab (Second Life), Amazon (The Kindle), Zappos and PayPal. Classes combine lecture and case study discussions and the workload is above the GSB average.

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 on innovative strategies and tactics used by global and international companies.

This is an advanced first-year MBA course in data-mining, machine learning, and cloud computing. The course presents real-world examples where a significant competitive advantage has been obtained through large-scale data analysis. Examples include advertising, finance, health care, revenue management, and the Internet. Students taking this course need to be comfortable with mathematical notation, algebra, calculus, probability, and statistics. Willingness to work with analytical software such as Matlab or R is required.

This is the first quarter of a two-quarter course series (OIT 384/OIT 385). In this course, students learn how to develop comprehensive solutions (most commonly medical devices) to some of the most significant medical problems. The first quarter includes an introduction to needs finding methods, brainstorming and concept creation. Students learn strategies for understanding and interpreting clinical needs, researching literature and searching patents. Working in small entrepreneurial multidisciplinary teams, students gain exposure to clinical and scientific literature review, techniques of intellectual property analysis and feasibility, basic prototyping and market assessment. Students create, analyze and screen medical technology ideas, and select projects for future development. Final presentations at the end of the winter quarter to a panel of prominent inventors and investors in medical technology provide the impetus for further work in the spring quarter. Course format includes expert guest lecturers (Thu: 4:15 to 6:05 pm), faculty-led practical demonstrations and coaching sessions and interactive team meetings under the mentorship of Biodesign fellows (Tues: 4:15 to 6:05 pm). Projects from previous years included: prevention of hip fractures in the elderly; methods to accelerate healing after surgery; less invasive techniques for bariatric surgery; point of care diagnostics to improve emergency room efficiency; novel devices to bring specialty-type of care to primary care community doctors. More than 4,000 patients have been treated to date with technologies developed as part of this program and more than ten venture-backed companies were started by alums of the program. Students must apply and be accepted into the course. The application is available online at http://biodesign.stanford.edu/bdn/courses/bioe374.jsp.

Two quarter sequence (continuation of OIT385 - see OIT384 for complete description of the sequence). 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 OIT384 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 continue to work in multidisciplinary teams to select a final concept and develop a business plan. Final presentations are made to a panel of prominent venture investors and serve the role of a VC pitch. nnnNew students (i.e. students who did not take OIT384 in the winter quarter) will need to submit an application at http://www.stanford.edu/group/biodesign/courseapplication11.html. Students who took OIT384 in the winter quarter are automatically accepted into the spring quarter.

This course satisfies the MBA distribution requirement in Optimization and Simulation Modeling (OSM). It is challenging but doable for students without an undergraduate degree in science or engineering; it does not assume experience in environmental science or quantitative analysis beyond admission requirements for the MBA program. Students will learn 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. nnnStudents are strongly encouraged to take the follow-on course on renewable energy, OIT 540 Environmental Science for Managers II. nnnFor the joint MBA-MS in Environment and Resources degree, students are required to take OIT 540, and either OIT 538 or OIT 539.

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.

This course provides an introduction to renewable sources of electricity and fuel, and is required for the joint MBA-MS in Environment and Resources degree. nnnStudents are strongly encouraged, but not required, to take OIT 538 or OIT 539 prior to taking this course.

Supply chain management (SCM) deals with the management of materials, information and financial flows in a network consisting of suppliers, manufacturers, distributors, retailers and customers. The coordination and integration of these flows within and across companies are critical in effective supply chain management. In this course, we introduce key concepts and new developments in information technologies (IT) for use in SCM. In particular, the advances of information technologies such as enterprise systems, the Internet, collaborative network, operational analytics and wireless technologies have a profound impact on how supply chains are structured and run. You are all challenged to think, discuss, share, and debate on the issues brought up.

One of the most interesting and underexplored areas in modern technology is, as Dan Reicher at Stanford has put it, "where energy technology (ET) meets information technology (IT)". The main driver of widespread use of computing in the modern age is the rapid reduction in the cost of computing services caused by Moore's law. At the same time, a substantial increase in the energy efficiency of computing (doubling every year and a half for more than six decades) has led to a proliferation of mobile computers, sensors, and controls, with implications that have only recently begun to be understood.nnnThis class will explore the direct and indirect implications of applying information technology to the production, delivery, and use of energy and associated services. It will first review current knowledge about the direct energy use associated with information technology, including data centers, personal computers, cellular telephones, mobile sensors, and other IT equipment. It will also summarize the state of knowledge about the types, amount, and growth rates of energy services delivered in the US and globally. Finally, it will explore the applications to which information technologies have been put in the energy industry, ranging from the use of visualization and analysis techniques to improve the results of oil and gas exploration, to the computer-aided design of wind turbines and automobiles, to the implications of wireless sensors and controls for the more efficient and effective use of energy. The class will culminate in student projects, typically business plans for new ventures using IT to radically transform how we understand and respond to the world around us.

Seminar examines the development and commercialization of medical technologies in the global setting focusing primarily on Europe, India and China. Faculty and guest speakers from industry and government discuss the status of the industry, as well as opportunities in and challenges to medical technology innovation unique to each geography. Topics related to development of technologies for bottom of the pyramid markets will also be addressed.

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.

The course provides students a strong theoretical background in several fundamental aspects underlying inventory theory. Topics include deterministic inventory models (EOQ, Power-of-two policies, ELS, serial and assembly networks), the Newsvendor model, multi-period stochastic inventory theory, serial and multi-echelon models, approximation algorithms, batch ordering and lost-sales models, infinitesimal perturbation analysis, distribution-free inventory theory, models for joint pricing and inventory decisions. The course also provides an overview of relevant mathematical concepts used in inventory theory, including convexity, duality, probability theory, finite and infinite state Markov decision processes, and comparative statics.

To compete successfully in today's market place, companies need to manage effectively the efficiency of activities to design, manufacture, distribute, service and recycle their products or services to their customers. Supply chain management deals with the management of materials, information and financial flows in a network consisting of suppliers, manufacturers, distributors, and customers. The coordination and integration of these flows within and across companies are critical in effective supply chain management. nnnIn parallel to the development of new practices and concepts in industry, there have been emerging research that are based on (1) structuring new processes and supply chain networks with the new technologies; (2) exploring ways to do planning and make decisions consequently; (3) quantifying the benefits as a result; and (4) aligning the incentives of multiple players in a supply chain when the costs and benefits to these players are different. nnnThis course will examine evolutionary research that focuses on the above themes. We will explore how such problems can be formulated, models can be structured, and analysis can be performed to address information-based supply chain management issues. You are all challenged to think, discuss, share, and debate on the issues brought up. The end result of this course is, hopefully, that we can start defining new, interesting and exciting research paths, and maybe even beginning to pursue some of the research ideas generated.

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.

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.

This seminar will highlight the research evolution and advances on the ssmart use of information 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.

The first half of this course covers (a) the basic theory of Brownian motion, (b) Ito stochastic calculus, and (c) the rudiments of continuous-time stochastic control, all undertaken at a brisk pace, aimed at students who already know the basics or else have a strong enough math background to learn them quickly. The text for this part of the course will be Brownian Motion and Stochastic Flow Systems, by J. Michael Harrison, John Wiley and Sons, 1985. (The book is available as a scanned PDF file at http://faculty-gsb.stanford.edu/harrison/HarrisonBook.pdf.) The second half of the course will explore in depth several models arising in operations research and economic theory. MS&E 322 (Stochastic Calculus and Control) provides ideal preparation, but this course is also suitable for students who have taken Statistics 310 A, B (measure theoretic probability) and have no previous exposure to stochastic calculus or stochastic control.

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.

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/.

This is the Advanced Application option in the menu of courses that satisfy the Management Foundations requirement in Optimization and Simulation Modeling(OSM). 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 30-35 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 base or accelerated MODS in their first year.nnnOIT 542 draws on knowledge acquired and skills developed in two other Management Foundations courses that are taken simultaneously: Data and Decisions (OIT 265) and Microeconomics (MGTECON 200 or 203). Students are required to construct and analyze at least one model for every class session.

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.

Description is currently unavailable because of ongoing review of the OIT PhD program by OIT faculty. Description will become available when the review is completed at the end of the Summer.