## OIT 269: MSx: Operations and Strategies

Operations refer to the processes through which businesses produce and deliver products or services. Managing operations well is necessary in order for these processes to be completed in a timely manner, consume minimal resources and costs, and achieve their goal effectively. This course focuses on managerial issues arising in the operations of manufacturing and service industries. The objectives of the course are to introduce operational problems and challenges faced by managers, as well as language, conceptual models, analytical techniques and strategies that are broadly applicable in confronting such problems.

Terms: Win
| Units: 3

Instructors:
Gur, Y. (PI)

## OIT 274: Data and Decisions - Base (Flipped Classroom)

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.

Terms: Win
| Units: 3

Instructors:
Benkard, L. (PI)
;
Spiess, J. (PI)

## OIT 276: Data and Decisions - Accelerated (Flipped Classroom)

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.

Terms: Win
| Units: 3

Instructors:
Wager, S. (PI)
;
Yurukoglu, A. (PI)

## OIT 333: Design for Extreme Affordability

Design for Extreme Affordability is a two-quarter project-based course hosted by Stanford's d.school and jointly offered by the Graduate School of Business and the School of Mechanical Engineering. We focus on the development of products and services to improve the lives of the world's poorest citizens. This multidisciplinary project-based experience creates an enabling environment in which students learn to design products and services that will change lives. Topics include user empathy, product and service design, rapid prototype engineering and testing, social entrepreneurship, business modeling, ethics, partnerships, team dynamics and project management. Since the course was first offered, we have executed 150 projects with 61 partners in 32 emerging and developing economies around the world. Many of the projects have been implemented and are achieving significant social impact. Students have worked on Agricultural, Medical, Water, Sanitation, Energy, Lighting, Nutrition and Education based projects. For further information go to
extreme.stanford.edu

Terms: Win
| Units: 4

## OIT 367: Business Intelligence from Big Data

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.

Terms: Win
| Units: 3

Instructors:
Bayati, M. (PI)
;
Khosravi, K. (SI)

## OIT 368: Design for Disruption

"Disruption" is a widely used and frequently misunderstood term. Understanding it better can help you think about your organization or team¿s strategy whether you're trying to disrupt, avoid being disrupted, or simply scanning the horizon for new trends in your industry.This course takes a unique view on disruption by combining disruption theory research, innovation strategy, and the ways that business practitioners and Silicon Valley entrepreneurs have redefined disruption over the last decade. We'll bring these perspectives together in a framework for gauging the disruptive potential of an innovation - that is, how likely the innovation is to fundamentally change the structure of an industry. You'll learn the critical roles that customers, value chains, and technologies play in driving such changes.While the popular press tends to focus on disruption in the technology sector, you'll see that it happens in every industry and sector, it can be done by mature, established companies, and it's not just for technology startups. We'll study disruption in a wide variety of industries, like nonprofits, pharmaceutical companies, food processing companies, airline manufacturers (Boeing) and chemical manufacturers. And of course, we'll talk about Uber, Airbnb, Microsoft, and Amazon too.In some cases we'll take a very futuristic view of disruption in which you will see how a very recent discovery can lead to fascinating possibilities for disruption that may be 10-20 years down the line. Distinguishing between developments that will last and drive changes vs. developments that are temporary fads is something the frameworks in this course will help you unpack.You'll also have the opportunity to investigate how established firms (we call them incumbents) can avoid the perils of being disrupted and left behind. You will identify the qualities of incumbents that have faced disruption successfully, and the missteps of those that have not.Finally, you will work on a capstone mini project in which you will apply the course frameworks to develop a disruption hypothesis for the industry of your choosing. This could be the industry you worked in the past or an industry you plan to work in the future, an industry that you may want to disrupt, or simply an industry that's compelling to you. Topics covered include:The Disruption Framework and the Three Pillars of DisruptionDisruption via new entrantsIncumbent self-disruption, and when incumbents miss the disruptionNonprofit vs. for-profit disruptionThe Five Forces FrameworkDeveloping and testing a new disruption hypothesis using design thinking and lean startupCases and examples we¿ll examine include:Impossible FoodsHIV treatment pharmaceuticalsWarby ParkerStarbucksAmazon Web ServicesFundboxBoeingDow CorningMicrosoftCalifornia Health Care FoundationKodakFastbrick RoboticsPelotonPokémon GoKodakUberAirbnb

Terms: Win
| Units: 3

Instructors:
Zenios, S. (PI)
;
Braden, R. (SI)

## OIT 384: Biodesign Innovation: Needs Finding and Concept Creation

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 2020), 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 2020), 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 40 venture-backed healthcare companies and has helped hundreds of students launch health technology careers, can be found at
http://biodesign.stanford.edu/.

Terms: Win
| Units: 4

Instructors:
Yock, P. (PI)
;
Brinton, T. (SI)
;
Denend, L. (SI)
;
Fuerch, J. (SI)
;
Saul, G. (SI)
;
Venook, R. (SI)
;
Hermann, J. (TA)
;
Jacob, M. (TA)

## OIT 644: Research in Operations, Information and Technology

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.

Terms: Aut, Win, Spr
| Units: 1
| Repeatable
12 times
(up to 12 units total)

Instructors:
Bayati, M. (PI)
;
Mendelson, H. (PI)
;
Weintraub, G. (PI)
...
more instructors for OIT 644 »

Instructors:
Bayati, M. (PI)
;
Mendelson, H. (PI)
;
Weintraub, G. (PI)
;
Gur, Y. (SI)
;
Spiess, J. (SI)
;
Wager, S. (SI)

## OIT 666: Engineering Online Markets

This class will explore topics the intersection of operations, engineering, and economics relevant to modern internet marketplaces, including those for dating, labor, accommodation, services, and rides. The objective of the class is to introduce and revisit technical tools traditionally used in the operations literature that may help advance the research frontier, as well as to expose students to recent developments and state-of-the-art research in online markets. The class will not cover the important and heavily studied topics of stable marriage and auctions. List of topics (preliminary): Intro to two-sided platforms and search frictions in matching markets; Design of the "search environment" and information disclosure policies on platforms; Balancing supply and demand in a spatio-temporal environment; Pricing issues in platforms; Service platforms; Reputation systems.

Terms: Win
| Units: 3

Instructors:
Saban, D. (PI)

## OIT 691: PhD Directed Reading (ACCT 691, FINANCE 691, GSBGEN 691, HRMGT 691, MGTECON 691, MKTG 691, OB 691, POLECON 691, STRAMGT 691)

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.

Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit

Instructors:
Bayati, M. (PI)
;
Bimpikis, K. (PI)
;
Gur, Y. (PI)
;
Iancu, D. (PI)
;
Lee, H. (PI)
;
Mendelson, H. (PI)
;
Plambeck, E. (PI)
;
Saban, D. (PI)
;
Spiess, J. (PI)
;
Wager, S. (PI)
;
Wein, L. (PI)
;
Weintraub, G. (PI)
;
Whang, S. (PI)
;
Xu, K. (PI)
;
Zenios, S. (PI)