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 course is an advanced option in the menu of classes satisfying the Core requirement in Optimization and Simulation Modeling (OSM). It is an advanced version of OIT 245 and OIT 247 and it covers a similar (but slightly expanded) set of concepts pertaining to prescriptive analytics, including static optimization, Monte-Carlo simulation, decision trees and dynamic optimization, and reinforcement learning. The main differences are in the pace and depth, with OIT 248 covering each topic significantly faster and at a deeper level. Additionally, OIT 248 leverages Python instead of Excel for implementation and devotes more time to discussing practical issues that arise in real-world, data-driven implementations. By the end of the course, students should develop an in-depth mental framework of the topics and leave with a good understanding of how they fit within modern machine-learning / AI pipelines that aid decision-making in complex problems. The class is taught in an interactive style, focusing on a variety of applications drawn from advertising, healthcare, finance, supply chain management, revenue management and pricing, scheduling, and risk management. We emphasize that OIT 248 uses Python to teach analytics, but is not a course on Python or coding per se. Some prior coding experience is helpful but is not a strict requirement for the course.

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.

Operations is the design and management of processes for production and delivery of services or goods. This course covers fundamental concepts and tools for excellent operations: Process Analysis - analysis, improvement, and design of operational processes; aligning operational processes with your business model. Managing variability and uncertainty in demand and supply. Climate Change - challenge and solutions. Quality Management - quantitative tools to measure and manage quality; best practices in quality management and innovation processes. Value Chain - managing flows of material and information through global value chains. The course has heavy analytic and quantitative work. No prior knowledge of operations is expected.

The course studies one of the most impactful business models in recent decades. We will study what makes an online marketplace successful, from network effects to reducing search and matching frictions, fostering trust, and effective ways to monetize. Students will explore both strategic decisions and the inner operations of these platforms, getting hands-on with the analytical and data science tools that power them. We will look at well-known models like those of Amazon, Google, Uber, and Airbnb, while also touching on the latest trends in the space. A particular emphasis will be on how AI is reshaping the way online marketplaces interact with users and the broader changes it might bring. Overall, the course will provide basic business knowledge for future investors, entrepreneurs, product managers, and anyone interested on online marketplaces.

Base Data and Decisions is a first-year MBA course in statistics and regression analysis. The course is taught using a flipped classroom model that combines extensive online materials with a 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, problem solving, 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 statistical software package R.

Accelerated Data and Decisions is a first-year MBA course in statistics and regression analysis. The course is taught using a flipped classroom model 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, problem solving, 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 statistical software package R. The accelerated course is designed for students with strong quantitative backgrounds. Students taking this course need to be comfortable with mathematical notation, algebra, and basic probability. Students without quantitative backgrounds should consider enrolling in the base version of the course.

This course is the first part of a new two quarter course series (OIT 280 & OIT 281) that offers students a holistic perspective on the rapidly evolving and integrated world of operations, technology and innovation. OIT 280 covers fundamental concepts and tools for excellent operations and new content on how business models, operational processes, technology and innovation come together in the real world. OIT 280 is more methodological, focusing on key operational processes and how they interact with business models and innovation processes. OIT 281 is more hands on, focusing on innovation processes. In OIT 281, students study and practice the creation of new business and operating models and engage in an innovation project. In OIT280, students focus on learning the key analytical tools and prepare a proposal for their innovation project. The course is under construction. Don't take it if you cannot tolerate sharp turns.

This course is the second part of the two-quarter course series (OIT 280 & OIT 281) and expands on the learnings developed in Part I: OIT 280. Students will learn how to structure business models and innovation processes and will apply the frameworks in a team project. A team project on an innovation challenge selected by students will provide a real world experience applying these frameworks. We encourage diverse innovation challenges that could lead to one of the following: a concept for a new venture, a critical evaluation of an existing business model with a recommendation for a change, a critical evaluation of operational processes for an existing organization with recommendations for changes. Students will develop a project proposal as part of OIT280 and they will launch and implement the project in OIT281. In addition, students will examine through a series of case studies how organizations develop operating models that implement innovative business models and integrate operations, innovation and technology. Key Topics: business model analysis and design, design thinking, lean startup, precedent-based innovation, technology readiness level assessment, AI and 3D printing, value chain innovation, innovation process applications.

Design for Extreme Affordability (Extreme) is for students who have a passion for social impact, and want to experience designing products and services that address issues of global poverty, through tackling real world challenges in collaboration with low-resource communities. Extreme is a two-quarter graduate level sequence cross listed by the Graduate School of Business (OIT333/334) and the School of Engineering (ME206A/B). The program is hosted by the d.school and open to students from all Stanford schools. This multidisciplinary team, fast paced, project based experience creates an enabling environment in which students learn to design products and services that will change the lives of the world's poorest citizens. Students work directly with course partners, and the communities they serve, on real world problems, the culmination of which is actual implementation and real impact. Topics include design thinking, product and service design, rapid prototype engineering and testing, business modeling, social entrepreneurship, team dynamics, impact measurement, operations planning and ethics. Products and services designed in the class have impacted well over 150 million people worldwide. Limited enrollment by application. Must sign up for both OIT333/ME206A (Winter) and OIT334/ME206B (Spring). See extreme.stanford.edu for more details and application process which opens in October. Cardinal Course certified by the Haas Center for Public Service.

Design for Extreme Affordability ('Extreme') is for students who have a passion for social impact, and want to experience designing products and services that address issues of global poverty, through tackling real world challenges in collaboration with low-resource communities. Extreme is a two-quarter graduate level sequence cross listed by the Graduate School of Business (OIT333/334) and the School of Engineering (ME206A/B). The program is hosted by the d.school and open to students from all Stanford schools. This multidisciplinary team, fast paced, project based experience creates an enabling environment in which students learn to design products and services that will change the lives of the world's poorest citizens. Students work directly with course partners, and the communities they serve, on real world problems, the culmination of which is actual implementation and real impact. Topics include design thinking, product and service design, rapid prototype engineering and testing, business modeling, social entrepreneurship, team dynamics, impact measurement, operations planning and ethics. Products and services designed in the class have impacted well over 150 million people worldwide.Limited enrollment by application. Must sign up for both OIT333/ME206A (Winter) and OIT334/ME206B (Spring).See extreme.stanford.edu for more details and application process which opens in October.Cardinal Course certified by the Haas Center for Public Service.

How can one best put AI and Data Science to work in a modern company and manage data science teams effectively? Leaning on the emerging theory and best practices, we will examine companies at various sizes and stages, from seed through IPO, and study real-life cases to understand how companies should leverage AI, data science and machine learning to build effective teams, core competencies, and competitive advantages. We will draw similarities and contrasts between regular technology and data-science-heavy companies in terms of management, technical risks, and economics, and more. The students will learn how to reason about the cost and benefits of building up a data science capability within a company, how to best manage teams to maximize performance and innovation, as well as how to evaluate the value creation through data and AI from the perspective of investors. We will have several AI entrepreneurs, executives, and investors participating in discussions. This is a 3-unit version of OIT 551. An up-to-date syllabus for OIT 351 can be found on this site: https://www.aistanford.org/.

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 Python 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 from medicine, business, and engineering work together to identify real-world unmet healthcare needs, invent new health technologies to address them, and plan for their development and implementation into patient care. During the first quarter (winter), 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), teams screen their ideas, 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 health technology industry experts and investors. Class sessions include faculty-led instruction and case studies, 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 50 venture-backed healthcare companies and has helped hundreds of students launch health technology careers, can be found at http://biodesign.stanford.edu/.

In this two-quarter course series (OIT 384/5), multidisciplinary student teams from medicine, business, and engineering work together to identify real-world unmet healthcare needs, invent new health technologies to address them, and plan for their development and implementation into patient care. During the first quarter (winter), 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), teams screen their ideas, 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 health technology industry experts and investors. Class sessions include faculty-led instruction and case studies, 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 50 venture-backed healthcare companies and has helped hundreds of students launch health technology careers, can be found at http://biodesign.stanford.edu/.

Exact topics TBD, but will include real-time optimization in different settings.

This year-long course takes a hands-on approach to learning about conducting research in Operations, Information and Technology. It will cover a broad spectrum of cutting-edge research in OIT from conceiving an idea to formulating a research problem, deriving results, and publication. The topical content will be customized to the specific interests of the enrolled students, but generally will be concerned with questions of operational interest.

This course is designed for OIT students of all cohorts. It will focus on alternative approaches to modeling the types of problems that arise in OIT research, based on the analysis of papers in the area.

Driven by technology, data insights, and collaborations, supply chains have evolved from traditional cost centers to vital sources of competitive advantage for leading global companies. Yet, as recent events like pandemics, wars or severe (climate-change induced) weather events serve to remind us, such advancements have also led to heightened complexities and management challenges. Correspondingly, supply chain research has transitioned during the past 60+ years from addressing primarily operational questions related to production, inventory, or logistics to examining strategic issues on information sharing or incentive alignment among the many stakeholders involved in today¿s global supply chains, and to understanding the role of regulation or technology in improving designs and processes. Reflecting these trends, this course sets two main learning objectives. First, to survey some of the foundational tools and techniques used to model and understand supply chains, leveraging ideas from operations research, decision sciences, economics, and computer science. Second, to identify knowledge gaps and research opportunities by covering emerging topics such as supply chain financing, designing and operating socially responsible and environmentally sustainable supply chains, or using technology (AI, online platforms, distributed ledgers, remote sensing) to improve designs and processes. The precise selection of topics varies by year, depending on instructor and student interest. The course is structured as a combination of formal lectures covering some of the foundational topics and seminar-style discussions involving student presentations.

This course provides an overview of asymptotic models and methods used in various areas of operations management. It includes traditional heavy traffic asymptotics for queueing networks, the Halfin-Whitt regime, the supermarket model, inventory theory, revenue management, applications of measure-valued processes in queues, and applications of mean field equilibrium models in matching markets and auctions for ad exchanges. The lectures will focus on modeling and performance analysis, and not on convergence proofs. 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 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