Results for OIT

This course provides basic skills in quantitative modeling. The objective is to familiarize students with the main steps in an analytical approach to business decision making: constructing an abstract model for a relevant business problem, formulating it in a spreadsheet environment such as Microsoft Excel, and using the tools of optimization, Monte Carlo simulation and sensitivity analysis to generate and interpret recommendations. The class will be taught in a lab style, with short in-class exercises done in small teams, focusing on a variety of applications drawn from online advertising, healthcare, finance, supply chain management, revenue and yield optimization.

The course is aimed at students who already have a background or demonstrated aptitude for quantitative analysis, and thus are comfortable with a more rapid coverage of the topics, in more depth and breadth, than in OIT 245.

This is the Advanced Applications option in the menu of courses that satisfy the Management Perspectives requirement in Optimization and Simulation Modeling (OSM). The course is tailored to students who already have command of basic optimization and modeling techniques, or who have a quantitative background that will allow them to catch up quickly. Some basic programming will be required, so experience with at least one programming language is recommended. The course will focus on using optimization techniques in practice. We will start by discussing different types of optimization models, including linear, integer, and quadratic optimization models. We will then discuss modeling techniques to make these models more realistic, such as multi-objective approaches and using regression models within optimization formulations. Lastly, we will cover tips and tricks for solving large models in practice, such as setting solving limits and heuristics.

Data and Decisions teaches you how to use data and quantitative reasoning to make sound decisions in complex and uncertain environments. The course draws on probability, statistics, and decision theory. Probabilities provide a foundation for understanding uncertainties, such as the risks faced by investors, insurers, and capacity planners. We will discuss the mechanics of probability (manipulating some probabilities to get others) and how to use probabilities to make decisions about uncertain events. Statistics allows managers to use small amounts of information to answer big questions. For example, statistics can help predict whether a new product will succeed or what revenue will be next quarter. The third topic, decision analysis, uses probability and statistics to plan actions, such as whether to test a new drug, buy an option, or explore for oil. In addition to improving your quantitative reasoning skills, this class seeks to prepare you for later classes that draw on this material, including finance, economics, marketing, and operations. At the end we will discuss how this material relates to machine learning and artificial intelligence.

This course focuses on the way information technology affects the structure of business models. It considers the impact of information technology on industries ranging from retail to logistics and from healthcare to smartphones. 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. The course is designed to help you make a transition into technology-related fields.

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 and statistics 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. In statistics, we focus on the linear regression model. Regression analysis provides a method for determining the relationship between a dependent variable and predictor variables. We introduce topics from non-linear models and machine learning model selection. Students taking this course need to be comfortable with mathematical notation, algebra, some calculus, and be open to learning to write short programs in statistical software (eg R or Stata). 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 plus some additional topics such as discrete dependent variable models. While OIT 267 focuses on real world applicability, we will explore the mathematical underpinnings of these topics in more depth than OIT 265 as an avenue for deeper understanding. The group regression project is a key component of the course.

Operations refer to the processes in which businesses use to produce and deliver products or services. They can also be processes in which businesses use to support the functioning of the company. These processes consume materials, require resources such as assets or people, and take time for completion. Managing operations well is necessary in order that these processes can be completed in a timely manner, consume the right amount of resources and costs, and achieve the optimal purpose. This course is about (1) the concepts and methods in which operations can be managed effectively, and (2) how to develop strategies around operations.

How does Uber match drivers to passengers? How does Airbnb select the set of listings to show to a guest in a search? How does eBay manage trust and reputation between buyers and sellers? How does Google optimize auctions for billions of dollars’ worth of online advertising? This course focuses on the basic analytic and data science tools used to address these and other challenges encountered in the most exciting online marketplaces in the world. With hands-on exercises we will open and understand the “black-box” of online marketplaces’ operations. We will cover application areas such as transportation, rentals, sharing, e-commerce, labor markets, and advertising, leveraging tools from D&D, OSM, and Micro (all base). Overall, the course will provide basic business knowledge for future investors, product managers, sales and marketing managers, operation managers, and anyone interested on online marketplaces.

This course is about how to use entrepreneurship and innovations in the value chains to create values in developing economies. The course will cover important principles and ways in which the value chains can be re-engineered or new business models can be designed to create values. In addition to materials covering a diversity of industries and geographical regions, the course will also enable students to be exposed to some of the interventions that the Stanford Institute of Innovation in Developing Economies (SEED) is working on in West Africa. Work and exam requirements: Students are expected to develop a project report on either portfolio companies related to SEED or other enterprises to show how value chain innovations can be advanced.

Data and Decisions is a first-year MBA course in statistics and regression analysis. The base D&D lab-based pilot is a new version of the course that combines extensive online materials with a more lab-based classroom approach. Traditional lecture content will be learned through online videos, simulations, and exercises, while time spent in the classroom will be discussions or computer lab sessions. Content covered includes basic probability, sampling techniques, hypothesis testing, t-tests, linear regression, and prediction models. The group regression project is a key component of the course, and all students will learn the software R.

Data and Decisions is a first-year MBA course in statistics and regression analysis. The accelerated D&D lab-based pilot is a new version of the course that combines extensive online materials with a more lab-based classroom approach. Traditional lecture content will be learned through online videos, simulations, and exercises, while time spent in the classroom will be discussions or computer lab sessions. Content covered includes sampling techniques, hypothesis testing, t-tests, linear regression, and prediction models. The group regression project is a key component of the course, and all students will learn the software R. nThe accelerated course is designed for students with strong quantitative backgrounds. Students taking this course need to be comfortable with mathematical notation, algebra, and some calculus. Students without quantitative backgrounds should consider enrolling in the base version of the course.

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.

The course focuses on the analysis and design of business models that are enabled by information technology. It considers the impact of information technology on multiple industries and how you can take advantage of new opportunities that are enabled by new technologies. nThe course is a compressed 2-unit course, with three double-sessions during the first week of the course and three double-sessions during the third week. During the intermediate week, students work on a final project where they design or analyze a business model. nnEach double-session analyzes a different aspect of business models that are enabled by information technology. Topics include online platforms, business models for online retail, electronic commerce logistics, disruptive technologies, value chain coordination in healthcare, and mobile value chains.nnThe course requires a strong analytic background and knowledge of fundamental aspects of information technology. MSx students may petition to take the course.

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.

The objective of this course is to analyze real-world situations where significant competitive advantage can be obtained through large-scale data analysis, with special attention to what can be done with the data and where the potential pitfalls lie. Students will be challenged to develop business-relevant questions and then solve for them by manipulating large data sets. Problems from advertising, eCommerce, finance, healthcare, marketing, and revenue management are presented. Students learn to apply software (such as R and SQL) to data sets to create knowledge that will inform decisions. The course covers fundamentals of statistical modeling, machine learning, and data-driven decision making. Students are expected to layer these topics over an existing facility with mathematical notation, algebra, calculus, probability, and basic statistics.

In this two-quarter course series (OIT 384/5), multidisciplinary student teams identify real-world unmet healthcare needs, invent new medtech products to address them, and plan for their development into patient care. During the first quarter (winter 2017), students select and characterize an important unmet healthcare problem, validate it through primary interviews and secondary research, and then brainstorm and screen initial technology-based solutions. In the second quarter (spring 2017), teams select a lead solution and move it toward the market through prototyping, technical re-risking, strategies to address healthcare-specific requirements (regulation, reimbursement), and business planning. Final presentations in winter and spring are made to a panel of prominent medtech experts and investors. Class sessions include faculty-led instruction and case demonstrations, coaching sessions by industry specialists, expert guest lecturers, and interactive team meetings. Enrollment is by application only, and students are expected to participate in both quarters of the course. Visit http://biodesign.stanford.edu/programs/stanford-courses/biodesign-innovation.html to access the application, examples of past projects, and student testimonials. More information about Stanford Biodesign, which has led to the creation of more than 40 venture-backed healthcare companies and has helped hundreds of student launch health technology careers, can be found at http://biodesign.stanford.edu/.

In this two-quarter course series ( OIT 384/5), multidisciplinary student teams identify real-world unmet healthcare needs, invent new medtech products to address them, and plan for their development into patient care. During the first quarter (winter 2017), students select and characterize an important unmet healthcare problem, validate it through primary interviews and secondary research, and then brainstorm and screen initial technology-based solutions. In the second quarter (spring 2017), teams select a lead solution and move it toward the market through prototyping, technical re-risking, strategies to address healthcare-specific requirements (regulation, reimbursement), and business planning. Final presentations in winter and spring are made to a panel of prominent medtech experts and investors. Class sessions include faculty-led instruction and case demonstrations, coaching sessions by industry specialists, expert guest lecturers, and interactive team meetings. Enrollment is by application only, and students are expected to participate in both quarters of the course. Visit http://biodesign.stanford.edu/programs/stanford-courses/biodesign-innovation.html to access the application, examples of past projects, and student testimonials. More information about Stanford Biodesign, which has led to the creation of more than 40 venture-backed healthcare companies and has helped hundreds of student launch health technology careers, can be found at http://biodesign.stanford.edu/.

Data for Action is an MBA compressed course dedicated to identifying value in and creating value from data. It deals with the technical, legal, regulatory and business strategic decisions that must be considered when delivering solutions to customers.

This course offers an overview of information technologies for enterprises and supply chain management. The course has two key components - a series of guest speakers and a set of readings. Students are expected to have read the assigned note on related technologies before class, and prepare to discuss technologies with the guest speaker in class. We will not discuss the technology per se in class, so students who enroll are expected to have some exposure to technologies in order to digest the materials on their own. The main topics of technologies are: DBMS (Database Management System), ERP (Enterprise Resource Planning), EAI (Enterprise Application Interface), data mining, Big Data, platform-based business model, cloud computing, RFID/NFC, mobile technologies, and mobile payment. In particular, students are encouraged to think hard about potential new businesses around the technology and discuss them in class.

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.

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.

The first part of the course reviews fundamental models in inventory management. Topics include deterministic models (EOQ, power-of-two policies, ELS, serial and assembly networks), Newsvendor, multi-period stochastic models under backlogging and lost-sales, multi-echelon and supply chain models, and infinite-horizon formulations. In the process, the course also reviews several fundamental mathematical concepts in inventory theory, including convexity, duality, finite / infinite state Markov decision processes, and comparative statics.nThe second part discusses advanced modeling concepts, and several new application areas. Topics include distribution-free and robust models, supply uncertainty and disruptions, flexibility and supply chain design, joint pricing and inventory, and problems at the interface of supply chains and finance.

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 focuses on studying a broad set of econometric methods to conduct empirical research in Operations Management and related fields in Management Science. The course complements formal econometrics and statistics classes by focusing on the application of different econometric methods and identification strategies to research problems that are relevant in different areas within Operations Management, including Supply Chain Management, Service Operations, Healthcare and Retail. Although statistics/econometrics classes provide a rigorous revision of the methods, they put less emphasis on how to apply these methods in different settings. This course aims to fill that gap by providing a problem-oriented approach, where the focus is on identifying empirical questions relevant to Operations Management and choosing an appropriate empirical strategy to address them. The course has a seminar format combining paper presentations by students, computer assignments and a short research proposal.

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.

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.

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.

Queueing models may be used to represent service delivery systems, 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) and control of queueing systems using asymptotic methods, both in the traditional heavy traffic regime and in the Halfin-Whitt regime. The second half consists of student presentations of recent papers in asymptotic methods in queueing systems. 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 smart use of information in supply chain management. Advances in technologies like real-time information systems, decision support methodologies, the internet and mobile technologies such as RFID (radio-frequency identification) have also enabled visibility and structural changes that result in significant supply chain performance enhancements. In parallel to the development of new practices and concepts in industry, we will examine 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.

The goal of this course is to provide a comprehensive introduction to the theory and practice of revenue management. It will comprise of a set of lectures that will cover the theoretical fundamentals of the area as well as an overview of current research developments through the presentation and discussion of recent papers. Topics include capacity control (single-resource and network), consumer behavior and market response models, dynamic pricing, procurement auctions, price experimentation, supply chain management and pricing.

The first half of this course will cover (i) the basic theory of Brownian motion, (ii) Ito stochastic calculus, and (iii) 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 Models of Performance and Control, by J. Michael Harrison, Cambridge University Press, 2013, which can be ordered from Amazon: http://www.amazon.com/Brownian-Performance-Control-Michael-Harrison/dp/1107018390/ref=sr_1_1?ie=UTF8&qid=1395420072&sr=8-1&keywords=Brownian+Models+of+Performance+and+ControlnnnThe second half of the course will explore in depth some models arising in operations research, finance and economic theory, such as the McDonald-Siegel investment model (an optimal stopping problem, treated in Chapter 5 of the textbook), Brownian versions of the classic cash balance problem (a family of stochastic control problems, treated in Chapter 7 of the textbook), and Yuliy Sannikov’s continuous-time principal-agent model (Review of Economic Studies, 2008). The course will be rather informally organized, more of a collaboration between students and instructor than a top-down lecture format, with at least half of the class time devoted to presentation of problems by students and auditors.

This course aims to introduce students to research topics in data-driven decision making with specific attention to healthcare applications. However, most concepts are applicable in areas beyond healthcare as well. Examples of topics are: prediction and risk adjustment, computational and statistical challenges associated with large-scale data, and dynamic decision making under uncertainty.

In most real-world problems, decision-makers often face uncertainty with respect to the underlying modelsnnthat drive the rewards/costs associated with potential strategies. The uncertainty in the problem can bennmodeled in a number of ways (e.g., a probability distribution over some parameters or an uncertainty setnnfor some variables) and a selection of an appropriate framework depends on various considerations rangingnnfrom the availability of historical data (or lack thereof) to the robustness of resulting strategies or thenntractability of the formulation. In addition, once a framework is selected, further challenges often arise whennnconsidering dynamic settings, in which the level of uncertainty may be updated from one period to another.nnThe high-level objectives of this course are:nn1. to introduce various frameworks for decision-making under model uncertaintynn2. to introduce tools to solve such problems, including ones to develop optimal or near-optimal learningnnstrategiesnn3. to discuss the various tradeoffs that arise such as tractability vs. performance, exploration vs. exploitation, and remembering vs. forgettingnn4. to explore research papers that demonstrate applications of discussed methods and models to variousnnproblems areas such as dynamic pricing, revenue management, inventory management, and assortmentnnselection

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.

Doctoral Practicum in Teaching

Doctoral Practicum in Research