Results for OIT

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.

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 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.

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.

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 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. While the advanced course will generally cover the same topics as OIT 256, it will go into more advanced techniques in a number of areas.

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.

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 (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 300,000 patients have been treated to date with technologies developed as part of this program and more than thirty 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.

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.

Many post-MBA jobs require the ability to analyze very large data sets and proficiency with Microsoft Excel is no longer sufficient. MBA graduates frequently shy away from more advanced analysis tools because of the need to write computer code to manipulate data. The course will seek to break that mental barrier by introducing students to the basic programming skills that would allow them to be productive later on. The focus of the course will be on SQL and R. This course assumes no prior programming experience.

This course offers an overview of eight technologies for enterprise computing. They are: ERP (Enterprise Resource Planning), EAI (Enterprise Application Interface), data mining, cloud computing, eCommerce, RFID/NFC, mobile technologies, and social network data analytics. On each topic, we discuss underlying technologies and applications using a variety of business cases.

In this course we will have a series of guest speakers who will discuss real businesses applying various technologies. Students come to class prepared by reading the assigned material and discuss the topic with the speakers in class. The course will provide opportunities to learn how different technologies are integrated to create values to end users. Students are expected to have some basic understanding of technologies and review them with readings on their own, so we will not discuss the technology per se in class.

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.

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.