research operations and management

Operations Management Research

Advancing Practice through Theory

• Presents research that advances both theory and practice of operations management.
• Includes all aspects of operations management, from manufacturing and supply chain to health care and service operations.
• Welcomes a variety of research methodologies, including case, action, survey, mathematical modelling, simulation, etc.
• Matteo Kalchschmidt

Latest issue

Volume 17, Issue 1

Latest articles

Integrating the interpretive hierarchical model and existing production systems of the engineered bamboo industry in an industry 4.0 environment.

Network working capital management, supply chain concentration, and corporate performance of focal companies

• Zhefan Piao
• Zihan Zheng

Drivers of supply chain adaptability: insights into mobilizing supply chain processes. A multi-country and multi-sector empirical research

• Michiya Morita
• Jose A. D. Machuca
• Rafaela Alfalla-Luque

Supply chain capabilities matter: digital transformation and green supply chain management in post-pandemic emerging economies: A case from Egypt

• Ahmed Hamdy

Environmental sustainability consideration with just-in-time practices in industry 4.0 era – A state of the art

• Vivek Singhal
• Lohithaksha M Maiyar

Journal updates

Call for papers: special issue on hybrid operations, emerging technologies, and the sustainability frontier – operational efficiency in the context of corporate social and environmental responsibilities.

Guest Editors: Mauro Fracarolli Nunes , EDC Paris Business School, France Camila Lee Park , EDC Paris Business School, France Jose A.D. Machuca , Universidad de Sevilla, Spain

Submission dates: October 15, 2024 to January 15, 2025

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Operations Management Research  is indexed by Scopus and has a CiteScore of 5.0 for 2022.

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World Leaders in Research-Based User Experience

ResearchOps 101

August 16, 2020 2020-08-16

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ResearchOps is a specialized area of DesignOps focused specifically on components concerning user-research practices.

ResearchOps (ReOps):  The orchestration and optimization of people, processes, and craft in order to amplify the value and impact of research at scale.

In This Article:

Researchops efforts, why researchops matters now, researchops is not just participant recruitment, common components of researchops, note: this model is not comprehensive, how to get started with researchops, the researchops community.

ResearchOps a collective term for efforts aimed at supporting researchers in planning, conducting, and applying quality user research, such as:

• Standardizing research methods and supporting documentation (e.g., scripts, templates, and consent forms) to save time and enable consistent application across teams
• Recruiting and managing research participants across studies
• Ensuring research ethics are understood and upheld by individual researchers across studies
• Educating research-team partners and leadership about the value of user research
• Managing user-research insights and making data accessible throughout the team and the organization
• Socializing success stories and ensuring that the overall impact of user research is known

The exponential growth of the UX profession means that more companies are realizing the value of UX and that the demand for UX and user research is increasing. This is great news: the value of our work is known and deemed necessary much more so than it was in the recent past.

The practical task of scaling research practices to meet this increased demand, however, often falls to existing UX research staff, with little guidance or additional bandwidth. Senior user researchers or research managers must deal with the responsibility and challenge of developing processes to scale their practices to match demand —  all while simultaneously continuing to plan and facilitate research sessions.

If a company does 10× the amount of user research it used to, the cost shouldn’t be 11× the old budget, as is all too likely if more projects lead to more bureaucracy, coordination, and other overhead costs. The new cost should be 9× due to economies of scale and reuse of prep work across studies. In fact, the ResearchOps cost goal should really be 8× or lower.

ResearchOps can provide relief, with dedicated roles (or at least focused efforts, if dedicated roles are not feasible) to create and compile intentional strategies and tools for managing the operational aspects of research, so that researchers can focus on conducting studies and applying research insights.

Many people equate ResearchOps with participant management (e.g., screening and scheduling participants for research studies), because this aspect is often an immediately obvious pain point for researchers and takes much time. While participant management is certainly an important component of ResearchOps, it is not the only aspect. The full landscape of operational elements necessary for creating and scaling a research practice is much broader.

As a former contract ResearchOps Specialist at Uber aptly explained to me during a series of interviews that I conducted with DesignOps and ResearchOps professionals: “The value ResearchOps can bring is not just calling and getting a participant but building a program and establishing consistent quality for communications and research methods.”

ResearchOps addresses a tapestry of interwoven operational aspects concerning user research, where every component both affects and is affected by the other elements.

The ResearchOps model described below was created by identifying key focus areas from our DesignOps and ResearchOps practitioner interviews. It outlines 6 common focus areas of ResearchOps:

• Participants: Recruiting, screening, scheduling, and compensating participants
• Governance: Processes and guidelines for consent, privacy, and information storage
• Knowledge: Processes and platforms for collecting, synthesizing, and sharing research insights
• Tools: Enabling efficiencies in research through consistent toolsets and platforms
• Competency: Enabling, educating, and onboarding others to perform research activities
• Advocacy: Defining, sharing, and socializing the value of user research throughout the organization

As the cyclical design of the model conveys, these are not isolated elements, but interrelated factors that drive the need for and influence each other.

Participant Management

The first component of ResearchOps — but not the only one — is participant management. This area includes creating processes for finding, recruiting, screening, scheduling, and compensating research-study participants. It’s often low-hanging fruit, because it’s typically the most apparent and immediate need of overloaded research teams.

Common ResearchOps activities and efforts within participant management include:

• Building a database or panel of potential study participants or researching and selecting external recruiting platforms
• Screening and approving participants
• Managing communication with participants
• Building frameworks for fair and consistent incentive levels based on participant expertise and required time investment

Governance guidelines are a necessity for any study involving participants. For example, consent templates must be compliant with existing data-privacy regulations, such as GDPR, and written in plain, transparent language. Additionally, as participants’ personally identifiable information (PII) is collected, the organization must follow legal regulations and ethical standards concerning where that information is stored, how long it is stored, how it is protected, and how its storage is made transparent to the participant. (PII refers to any data that could be used to identify a person, such as a full name, date of birth, or email address.)

Common ResearchOps activities and efforts within governance include:

• Researching and understanding the application of data-privacy regulations, such as GDPR, to the UX-research process
• Establishing ethically sound processes and communications
• Writing and standardizing compliant and transparent consent forms for various study types and formats of data collected
• Managing the proper maintenance and disposal of PII and study artifacts, such as interview scripts or audio- and video-session recordings

Knowledge Management

As data begins to accumulate from studies, the need for knowledge management becomes increasingly apparent. This element of ResearchOps is focused on collecting and synthesizing data across research studies and ensuring that it is findable and accessible to others. Not only can effectively compiled and managed research insights help research teams share findings and avoid repetitious studies, but they can also serve to educate those outside the team.

Common ResearchOps activities and efforts within knowledge management include:

• Developing standardized templates for data collection during studies
• Building a shared database of research insights (sometimes called a research repository) where findings from studies across the organization can be stored 
• Developing regular meetings or other avenues for sharing and updating the organization about known user insights
• Coordinating with other teams conducting research (e.g.,  marketing or business intelligence) in order to create a comprehensive source of insights

Most of the activities discussed so far require tools or platforms. For example: What platform will be used to recruit and screen participants? What applications will be used to manage participant PII? What programs will be used to house all of the resulting research findings? Furthermore, tools that facilitate the actual research, such as remote usability-testing platforms, analytics, or survey platforms, or video-editing and audio- transcription tools, must be considered. While autonomy in choice can be valuable, auditing the research toolset to create some level of consistency across the team expedites sharing and collaboration. 

Common ResearchOps activities and efforts within tools include:

• Researching and comparing appropriate platforms for recruiting and managing participant information
• Selecting research tools for usability testing, surveys, remote interviews, or any other types of research
• Managing access privileges and platform seats across individual user researchers and teams
• Auditing the research toolkit to ensure that all platforms and applications in use are compliant with data-privacy regulations
• While buildings and facilities are usually not thought of as “tools,” ResearchOps should also manage any usability labs as well as non-lab testing rooms, including contracts for outsourced locations.

As the demand for and amount of research conducted continues to scale, it becomes critical to also grow the organization’s research capabilities and skills. The competency component is concerned with enabling more people to understand and do research. This effort often involves providing resources and education both to (1) researchers, so that they can continue to develop their skills, and (2) nonresearchers, so that they can integrate basic research activities into their work when researchers are unavailable (and know when to call for help instead of rolling their own study).

Common ResearchOps activities and efforts within competency include:

• Developing standardized and consistent professional-development opportunities for researchers who want to grow deeply or broadly in their expertise
• Establishing mentorship programs to onboard new researchers and help them learn and develop new research skills
• Creating a playbook or database of research methods to onboard new researchers or educate others outside of the team
• Developing formalized training or curricula to train nonresearchers and expose them to user-centered approaches and activities, so that basic research can be incorporated into work when researchers cannot scale to demand

The final component, advocacy, is concerned with how the value of UX research is defined and communicated to the rest of the organization. Simply put, what is being done to ensure that the rest of the organization is aware of the value and impact of research? For example, does the team socialize success stories and demonstrate the impact of user research? To come full circle on the cyclical nature of the model, proper advocacy helps ensure fuel and resources for all the other focus areas and ensures the ResearchOps practice can continue to scale effectively.

Common ResearchOps activities and efforts within advocacy include:

• Creating a UX research-team mission or statement of purpose that can be used to talk about the team’s purpose with other colleagues
• Developing case studies that demonstrate the impact of properly applied research findings on company metrics and KPI’s
• Developing a process for regularly sharing insights and success stories with the rest of the organization (e.g., lunch-and-learns, email newsletters, posters,)

The 6 components in this model are specialized areas that research practices must consider in order to create consistent, quality research efforts across teams; however, there are other elements that must be considered and intentionally designed that are critical to the health of any research team or practice.

One such area is documented career pathways. The documentation and use of career pathways in general is rare. (In our recent DesignOps research , only 11% of respondents reported having a documented, shared growth path — an abysmal percentage.) But, especially within relatively nascent domains, such as ResearchOps, where there is no decisive, publicly available legacy of successful team structures or models for roles and responsibilities, it’s equally both critical and challenging to create and document such pathways.

To make sure that you include additional elements that are not represented in this ResearchOps model, reference our DesignOps framework . It provides a comprehensive landscape of potential focus areas for operationalizing design in general; many of these areas equally apply to creating a healthy, focused ResearchOps practice. Team structure and role definitions, consistent hiring and onboarding practices, team communication and collaboration methods, and workflow balance and planning are just a few additional areas to consider.

As mentioned, ResearchOps is a whole of many parts that are best considered holistically, because every component both affects and is affected by the other factors. However, when establishing a ResearchOps practice, not all aspects can be addressed at once.

The first step to figuring out where to start is understanding where the biggest pain points are. Are researchers overwhelmed with the logistics of recruiting and scheduling participants? Maybe participant management is the best starting point for the team. Is research data scattered and inaccessible to new team members, causing duplicative research efforts and poor research memory? Perhaps knowledge management is where the team should focus.

Begin by identifying the current problems that necessitate ResearchOps. Perform internal research to understand where the biggest pain points currently exist for research teams and research-team partners. For example, you could send out a survey or have focus groups with researchers to collect information on whether current processes enable them to be effective and what gets in their way the most. Additionally, carry out internal stakeholder interviews to uncover the biggest pain points for partners within the research process. This knowledge will help you create a clear role for ResearchOps.

Just remember, when it comes to scaling research, balance your focus between the component that you chose to address and the overall tapestry of considerations. Evolve and expand your focus as needs shift to maintain a balanced practice.

The ResearchOps Community is a group of ResearchOps professionals and researchers who have conducted extensive research to understand the way the UX community thinks about and addresses ResearchOps challenges. They have compiled a collection of resources and thought leadership on the topic, available on the group’s website .

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Berkeley Berkeley Academic Guide: Academic Guide 2023-24

Operations research and management science.

About the Program

Bachelor of arts (ba).

The Operations Research and Management Science (ORMS) major is designed for students in the College of Letters & Science. It provides a solid foundation in the quantitative, model building, and problem-solving skills of operations research and management science. It also gives students the flexibility to learn more about a particular field of interest to them in which they can apply these skills.

The major is very math intensive and is appropriate for students who enjoy and are good at mathematics, computers, and solving practical, multidisciplinary problems. 

Declaring the Major

Note: These declaration requirements are for students admitted to UC Berkeley prior to Fall 2023.  Newly admitted first year students in Fall 2023 should refer to the L&S High Demand major policy.

ORMS is a high-demand major in L&S.

For students admitted to UC Berkeley prior to fall 2023:

For students admitted to UC Berkeley fall 2023 and thereafter:

First-year students applying to Berkeley Letters & Science will be guaranteed admission into the ORMS major if they selected ORMS as their primary major on their UC Berkeley admissions application. Students are guaranteed a spot in the ORMS major, subject to completing the major prerequisites, maintaining good academic standing in L&S, and filing a declaration form.

The opportunities for being admitted into the ORMS major after enrollment at UC Berkeley will be extremely limited, and applying to the ORMS major via the comprehensive review process does not guarantee a spot in the major. If you have an interest in the ORMS major, we strongly encourage you to select ORMS as your primary major during the UC application process. If you opt to change to the ORMS major after being admitted to Berkeley, you will be required to have an alternate plan to declare a non-high demand major as a back-up.

For more information on the high-demand major policy please visit the "Admissions" page for the College of Letters and Science on the Berkeley Academic Guide.

Prerequisite Coursework

All four prerequisite classes ( MATH 53 , MATH 54 , UGBA 10 , and either  ECON 1 ,  ECON 2 or ECON C3 ) must be completed prior to acceptance to the major and all must be taken for a letter grade. Students should declare the major during the semester in which they are enrolled in their final prerequisites but before their 4th semester.  For students applying to the major with prerequisite coursework completed Spring 2020 to Spring 2021, please see the ORMS website for alternate prerequisite GPA calculations of coursework not taken for a letter grade.   

Many factors are considered in determining admission. The main criterion, however, is academic performance as measured by the Berkeley GPA in the prerequisite courses.   Since this major is capped, planning for an alternate major is recommended. There is an Operations Research concentration in the Math Department that might be a good choice if students are not admitted to the ORMS major.

Honors Program

Students with a grade point average (GPA) of at least 3.5 overall and 3.7 in the major upper division coursework should consider participating in the ORMS honors program. To graduate with honors, a student must find a faculty sponsor appropriate for an original research project that he or she wishes to do and enroll in two semesters (6 units) of the honors thesis courses  IND ENG H196A  and  IND ENG H196B

Alternatively, a student may take two approved graduate courses in Operations Research or a related field, and achieve at least an A- in each course. Courses used for the honors program cannot be used to fulfill the requirements for the ORMS major or any IEOR graduate program. The student must also maintain a minimum 3.5 overall GPA and 3.7 in the major at the time of graduation.

Minor Program

There is no minor program in Operations Research and Management Science.  However, students interested in an ORMS minor, may be interested in the Industrial Engineering and Operations Research minor .

Visit Department Website

Major Requirements

In addition to the University, campus, and college requirements, listed on the College Requirements tab, students must fulfill the below requirements specific to their major program.

General Guidelines

• All courses taken to fulfill the major requirements below must be taken for graded credit, other than courses listed which are offered on a  Pass/No Pass  basis only. Other exceptions to this requirement are noted as applicable.  Exceptions for the Spring 2020 to Spring 2021 semesters are listed on the ORMS website .
• No more than one upper division course may be used to simultaneously fulfill requirements for a student's major and minor programs, with the exception of minors offered outside of the College of Letters & Science.
• A minimum grade point average (GPA) of 2.0 must be maintained in both upper and lower division courses used to fulfill the major requirements.

For information regarding residence requirements and unit requirements, please see the College Requirements tab.

Lower Division Requirements

Upper division requirements.

Students will receive no credit for IND ENG 165  after taking STAT 135 , or   IND ENG 172 after taking STAT 134 or STAT C140 .

Sample Clusters

Decision Making in Economic Systems

Decision Making in Industrial and Service Systems

Decision Making in Societal Systems

Algorithmic Decision Making 

College Requirements

Undergraduate students must fulfill the following requirements in addition to those required by their major program.

For detailed lists of courses that fulfill college requirements, please review the  College of Letters & Sciences  page in this Guide. For College advising appointments, please visit the L&S Advising Pages. 

University of California Requirements

Entry level writing.

All students who will enter the University of California as freshmen must demonstrate their command of the English language by fulfilling the Entry Level Writing requirement. Fulfillment of this requirement is also a prerequisite to enrollment in all reading and composition courses at UC Berkeley. 

American History and American Institutions

The American History and Institutions requirements are based on the principle that a US resident graduated from an American university, should have an understanding of the history and governmental institutions of the United States.

Berkeley Campus Requirement

American cultures.

All undergraduate students at Cal need to take and pass this course in order to graduate. The requirement offers an exciting intellectual environment centered on the study of race, ethnicity and culture of the United States. AC courses offer students opportunities to be part of research-led, highly accomplished teaching environments, grappling with the complexity of American Culture.

College of Letters & Science Essential Skills Requirements

Quantitative reasoning.

The Quantitative Reasoning requirement is designed to ensure that students graduate with basic understanding and competency in math, statistics, or computer science. The requirement may be satisfied by exam or by taking an approved course.

Foreign Language

The Foreign Language requirement may be satisfied by demonstrating proficiency in reading comprehension, writing, and conversation in a foreign language equivalent to the second semester college level, either by passing an exam or by completing approved course work.

Reading and Composit ion

In order to provide a solid foundation in reading, writing, and critical thinking the College requires two semesters of lower division work in composition in sequence. Students must complete parts A & B reading and composition courses in sequential order by the end of their fourth semester.

College of Letters & Science 7 Course Breadth Requirements

Breadth requirements.

The undergraduate breadth requirements provide Berkeley students with a rich and varied educational experience outside of their major program. As the foundation of a liberal arts education, breadth courses give students a view into the intellectual life of the University while introducing them to a multitude of perspectives and approaches to research and scholarship. Engaging students in new disciplines and with peers from other majors, the breadth experience strengthens interdisciplinary connections and context that prepares Berkeley graduates to understand and solve the complex issues of their day.

Unit Requirements

120 total units

Of the 120 units, 36 must be upper division units

• Of the 36 upper division units, 6 must be taken in courses offered outside your major department

Residence Requirements

For units to be considered in "residence," you must be registered in courses on the Berkeley campus as a student in the College of Letters & Science. Most students automatically fulfill the residence requirement by attending classes here for four years, or two years for transfer students. In general, there is no need to be concerned about this requirement, unless you go abroad for a semester or year or want to take courses at another institution or through UC Extension during your senior year. In these cases, you should make an appointment to meet an adviser to determine how you can meet the Senior Residence Requirement.

Note: Courses taken through UC Extension do not count toward residence.

Senior Residence Requirement

After you become a senior (with 90 semester units earned toward your BA degree), you must complete at least 24 of the remaining 30 units in residence in at least two semesters. To count as residence, a semester must consist of at least 6 passed units. Intercampus Visitor, EAP, and UC Berkeley-Washington Program (UCDC) units are excluded.

You may use a Berkeley Summer Session to satisfy one semester of the Senior Residence requirement, provided that you successfully complete 6 units of course work in the Summer Session and that you have been enrolled previously in the college.

Modified Senior Residence Requirement

Participants in the UC Education Abroad Program (EAP), Berkeley Summer Abroad, or the UC Berkeley Washington Program (UCDC) may meet a Modified Senior Residence requirement by completing 24 (excluding EAP) of their final 60 semester units in residence. At least 12 of these 24 units must be completed after you have completed 90 units.

Upper Division Residence Requirement

You must complete in residence a minimum of 18 units of upper division courses (excluding UCEAP units), 12 of which must satisfy the requirements for your major.

Student Learning Goals

Learning goals for the major.

All Operations Research and Management Science (ORMS) graduates are expected to acquire the following general skills and knowledge:

• Ability to apply mathematics and science to the solution of societal problems.
• Ability to design and conduct experiments, analyze, and interpret data.
• Ability to design system and operating policies to meet desired needs.
• Ability to function on multidisciplinary teams and communicate effectively.
• Ability to identify, formulate, and solve societal system problems.
• Understanding of professional and ethical responsibility.
• Recognize the need for and ability to engage in life-long learning.
• Knowledge of contemporary issues.
• Ability to use techniques, skills, and modern tools in practice.

The ORMS major in the IEOR Department has four general objectives for the Bachelor of Arts degree program. The department aims for the BA degree graduates to become skilled in the following:

• Quantitative modeling and analysis of a broad array of systems-level decision problems concerned with economic efficiency, productivity, and quality.
• Development and creative use of analytical and computational methods for solving these problems.
• Collection and analysis of data and the use of database and decision-support tools.
• Comprehension and analysis of risk and uncertainty.

In addition, graduates will obtain the broader skills, background, and knowledge necessary to be effective life-long professionals who understand the impact of systems in a societal context in a rapidly changing global economy.

Specific outcomes of the BA degree program are as follows:

• Develop scientific, quantitative, model building, and problem solving skills through core courses in mathematics, statistics, operations research, and management sciences.
• Learn how to apply these skills and tools effectively for operational, tactical, and strategic decisions in an area of choice.
• Pursue graduate study in operations research and the management sciences.

Major Maps help undergraduate students discover academic, co-curricular, and discovery opportunities at UC Berkeley based on intended major or field of interest. Developed by the Division of Undergraduate Education in collaboration with academic departments, these experience maps will help you:

Explore your major and gain a better understanding of your field of study

Connect with people and programs that inspire and sustain your creativity, drive, curiosity and success

Discover opportunities for independent inquiry, enterprise, and creative expression

Engage locally and globally to broaden your perspectives and change the world

• Reflect on your academic career and prepare for life after Berkeley

Use the major map below as a guide to planning your undergraduate journey and designing your own unique Berkeley experience.

View the Operations Research and Management Science Major Map PDF.

IND ENG 24 Freshman Seminars 1 Unit

Terms offered: Fall 2017, Fall 2016, Fall 2015 The Berkeley Seminar Program has been designed to provide new students with the opportunity to explore an intellectual topic with a faculty member in a small-seminar setting. Berkeley Seminars are offered in all campus departments, and topics vary from department to department and semester to semester. Freshman Seminars: Read More [+]

Objectives & Outcomes

Course Objectives: Provide an introduction to the field of Industrial Engineering and Operations Research through a series of lectures.

Student Learning Outcomes: Learn more about Industrial Engineering and Operations Research.

Rules & Requirements

Repeat rules: Course may be repeated for credit when topic changes.

Hours & Format

Fall and/or spring: 15 weeks - 1 hour of seminar per week

Additional Format: One hour of Seminar per week for 15 weeks.

Additional Details

Subject/Course Level: Industrial Engin and Oper Research/Undergraduate

Grading/Final exam status: The grading option will be decided by the instructor when the class is offered. Final exam required.

Freshman Seminars: Read Less [-]

IND ENG 66 A Bivariate Introduction to IE and OR 3 Units

Terms offered: Fall 2016 This Freshman-level Introductory course will provide an intuitive overview of the fundamental problems addressed and methods in the fields of Industrial Engineering and Operations Research including Constrained Optimization, Human Factors, Data Analytics, Queues and Chains, and Linear Programming. The course will focus on two-dimensional, i.e., bivariate, examples where the problems and methods are amenable to visualization and geometric intuition. The course will discuss applications such as dieting, scheduling, and transportation. This course will not require pre-requisites and will present the core concepts in a self-contained manner that is accessible to Freshmen to provide the foundation for future coursework. A Bivariate Introduction to IE and OR: Read More [+]

Course Objectives: • Provide a broad survey of the important topics in IE and OR, and develop intuition about problems, algorithms, and abstractions using bivariate examples (2D). • Describe different mathematical abstractions used in IEOR (e.g., graphs, queues, Markov chains), and how to use these abstractions to model real-world problems. • Introduce students to the data analysis process including: developing a hypothesis, acquiring data, processing the data, testing the hypothesis, and presenting results. • Provide students with concrete examples of how the mathematical tools from the class apply to real problems such as dieting, scheduling, and transportation.

Credit Restrictions: Course restricted to Freshman students.

Fall and/or spring: 15 weeks - 3 hours of lecture per week

Additional Format: Three hours of lecture per week.

Grading/Final exam status: Letter grade. Final exam required.

Instructor: Goldberg

A Bivariate Introduction to IE and OR: Read Less [-]

IND ENG 98 Supervised Group Study and Research 1 - 3 Units

Terms offered: Spring 2019, Fall 2015, Spring 2015 Supervised group study and research by lower division students. Supervised Group Study and Research: Read More [+]

Prerequisites: Consent of instructor

Credit Restrictions: Enrollment is restricted; see the Introduction to Courses and Curricula section of this catalog.

Repeat rules: Course may be repeated for credit without restriction.

Fall and/or spring: 15 weeks - 1-3 hours of directed group study per week

Additional Format: One to three hours of directed group study per week.

Grading/Final exam status: Offered for pass/not pass grade only. Final exam not required.

Supervised Group Study and Research: Read Less [-]

IND ENG 99 Supervised Independent Study and Research 1 - 4 Units

Terms offered: Prior to 2007 Supervised independent study for lower division students. Supervised Independent Study and Research: Read More [+]

Prerequisites: Freshman or sophomore standing and consent of instructor

Fall and/or spring: 15 weeks - 1-4 hours of independent study per week

Summer: 8 weeks - 1.5-7.5 hours of independent study per week 10 weeks - 1.5-6 hours of independent study per week

Additional Format: One to four hours of independent study per week. One and one-half to six hours of independent study per week for 10 weeks. One and one-half to seven and one-half hours of independent study per week for 8 weeks.

Supervised Independent Study and Research: Read Less [-]

IND ENG 115 Industrial and Commercial Data Systems 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Design and implementation of databases, with an emphasis on industrial and commercial applications. Relational algebra, SQL, normalization. Students work in teams with local companies on a database design project. WWW design and queries. Industrial and Commercial Data Systems: Read More [+]

Prerequisites: Upper division standing

Fall and/or spring: 15 weeks - 2 hours of lecture and 2 hours of laboratory per week

Additional Format: Two hours of lecture and two hours of laboratory/project per week.

Industrial and Commercial Data Systems: Read Less [-]

IND ENG 120 Principles of Engineering Economics 3 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Economic analysis for engineering decision making: Capital flows, effect of time and interest rate. Different methods of evaluation of alternatives. Minimum-cost life and replacement analysis. Depreciation and taxes. Uncertainty; preference under risk; decision analysis. Capital sources and their effects. Economic studies. Formerly Engineering 120. Principles of Engineering Economics: Read More [+]

Credit Restrictions: Students will receive 2 units for 120 after taking Civil Engineering 167. Students will not receive credit after taking Engineering 120.

Fall and/or spring: 15 weeks - 2 hours of lecture and 1 hour of discussion per week

Summer: 8 weeks - 4 hours of lecture and 2 hours of discussion per week

Additional Format: Two hours of lecture and one hour of discussion per week. Four hours of lecture and two hours of discussion per week for 8 weeks.

Instructor: Adler

Principles of Engineering Economics: Read Less [-]

IND ENG 130 Methods of Manufacturing Improvement 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Analytical techniques for the improvement of manufacturing performance along the dimensions of productivity, quality, customer service, and throughput. Techniques for yield analysis, process control, inspection sampling, equipment efficiency analysis, cycle time reduction, and on-time delivery improvement. Applications on semiconductor manufacturing or other industrial settings. Methods of Manufacturing Improvement: Read More [+]

Prerequisites: IND ENG 172 , MATH 54 , or STAT 134 ( STAT 134 may be taken concurrently)

Additional Format: Three hours of Lecture per week for 15 weeks.

Instructor: Leachman

Methods of Manufacturing Improvement: Read Less [-]

IND ENG 135 Applied Data Science with Venture Applications 3 Units

Terms offered: Spring 2023, Spring 2022, Fall 2021 This highly-applied course surveys a variety of key of concepts and tools that are useful for designing and building applications that process data signals of information. The course introduces modern open source, computer programming tools, libraries, and code samples that can be used to implement data applications. The mathematical concepts highlighted in this course include filtering, prediction, classification, decision-making, Markov chains , LTI systems, spectral analysis, and frameworks for learning from data. Each math concept is linked to implementation using Python using libraries for math array functions (NumPy), manipulation of tables (Pandas), long term storage (SQL, JSON, CSV files), natural language (NLTK), and ML frameworks. Applied Data Science with Venture Applications: Read More [+]

Student Learning Outcomes: Students will be able to design and build data sample application systems that can interpret and use data for a wide range of real life applications across many disciplines and industries; implement these concepts within applications with modern open source CS tools. understand relevant mathematical concepts that are used in systems that process data;

Prerequisites: Prerequisites include the ability to write code in Python, and a probability or statistics course. This course is ideal for students who have taken COMPSCI C8 / DATA C8 / INFO C8 / STAT C8

Grading/Final exam status: Letter grade. Alternative to final exam.

Instructor: Sidhu

Applied Data Science with Venture Applications: Read Less [-]

IND ENG 142 Introduction to Machine Learning and Data Analytics 3 Units

Terms offered: Fall 2023, Spring 2023, Fall 2022 This course introduces students to key techniques in machine learning and data analytics through a diverse set of examples using real datasets from domains such as e-commerce, healthcare, social media, sports, the Internet, and more. Through these examples, exercises in R, and a comprehensive team project, students will gain experience understanding and applying techniques such as linear regression, logistic regression, classification and regression trees, random forests, boosting, text mining, data cleaning and manipulation, data visualization, network analysis, time series modeling, clustering, principal component analysis, regularization, and large-scale learning. Introduction to Machine Learning and Data Analytics: Read More [+]

Course Objectives: 1. To expose students to a variety of statistical learning methods, all of which are relevant in useful in wide range of disciplines and applications. 2. To carefully present the statistical and computational assumptions, trade-offs, and intuition underlying each method discussed so that students will be trained to determine which techniques are most appropriate for a given problem. 3. Through a series of real-world examples, students will learn to identify opportunities to leverage the capabilities of data analytics and will see how data analytics can provide a competitive edge for companies. 4. To train students in how to actually apply each method that is discussed in class, through a series of labs and programming exercises. 5. For students to gain some project-based practical data science experience, which involves identifying a relevant problem to be solved or question to be answered, gathering and cleaning data, and applying analytical techniques. 6. To introduce students to advanced topics that are important to the successful application of machine learning methods in practice, include how methods for prediction are integrated with optimization models and modern optimization techniques for large-scale learning problems.

Prerequisites: IEOR 165 or equivalent course in statistics. Prior exposure to optimization is helpful but not strictly necessary. Some programming experience/literacy is expected

Fall and/or spring: 15 weeks - 3 hours of lecture and 1 hour of discussion per week

Additional Format: Three hours of lecture and one hour of discussion per week.

Instructors: Grigas, Paul

Introduction to Machine Learning and Data Analytics: Read Less [-]

IND ENG 142A Introduction to Machine Learning and Data Analytics 4 Units

Terms offered: Fall 2024, Spring 2024 This course introduces students to key techniques in machine learning and data analytics through a diverse set of examples using real datasets from domains such as e-commerce, healthcare, social media, sports, the Internet, and more. Through these examples, exercises in R, and a comprehensive team project, students will gain experience understanding and applying techniques such as linear regression, logistic regression, classification and regression trees , random forests, boosting, text mining, data cleaning and manipulation, data visualization, network analysis, time series modeling, clustering, principal component analysis, regularization, and large-scale learning. Introduction to Machine Learning and Data Analytics: Read More [+]

Prerequisites: IND ENG 165 and IND ENG 172 or equivalent courses in probability and statistics. Prior exposure to optimization (either IND ENG 160 or IND ENG 162 or equivalent). Some programming experience/literacy is expected

Credit Restrictions: Students will receive no credit for IND ENG 142A after completing IND ENG 142 , IND ENG 242, IND ENG 242A , COMPSCI 189 , COMPSCI 289, or STAT 154 . A deficient grade in IND ENG 142A may be removed by taking IND ENG 142 .

IND ENG 142B Machine Learning and Data Analytics II 4 Units

Terms offered: Spring 2024 Following IEOR 142A/242A, this course further introduces students to essential methodologies and recent trends in machine learning and data analytics. The course will bridge theoretical foundations with applied data analytics by using examples and real datasets from domains such as e-commerce, social media, finance, and more. Students will gain experience with various data analytics packages in Python and will deliver a comprehensive team project. Topics include: deep learning, time series and survival analysis, end-to-end learning, causal inference, reinforcement learning, and ethics, fairness and safety in artificial intelligence. Machine Learning and Data Analytics II: Read More [+]

Prerequisites: IndEng 142A or IndEng 242A or equivalent introductory machine learning class. Familiarity with the Python programming language

Credit Restrictions: Students will receive no credit for IND ENG 142B after completing IND ENG 242B .

Grading/Final exam status: Letter grade. Alternate method of final assessment during regularly scheduled final exam group (e.g., presentation, final project, etc.).

Machine Learning and Data Analytics II: Read Less [-]

IND ENG 145 Fundamentals of Revenue Management 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Revenue management (RM) is the decision science of efficiently selling a fixed supply of various goods and services when the demand is heterogeneous and uncertain. This undergraduate course will focus on fundamental models and algorithms for RM. Broad usefulness of concepts will be demonstrated through applications in airline reservation systems, retail, advertising, e-commerce and school-student assignments. Fundamentals of Revenue Management: Read More [+]

Prerequisites: IndEng 162, IndEng 169 and either IndEng 173 Or IndEng 172 (or equivalent introductory courses in mathematical programming and probability). Familiarity with algorithm design and mathematical maturity recommended

Instructor: Udwani

Fundamentals of Revenue Management: Read Less [-]

IND ENG 150 Production Systems Analysis 3 Units

Terms offered: Fall 2024, Fall 2020, Fall 2019 Quantitative models for operational and tactical decision making in production systems, including production planning, inventory control, forecasting, and scheduling. Production Systems Analysis: Read More [+]

Prerequisites: IND ENG 160 , IND ENG 173 , IND ENG 162 , IND ENG 165 , and ENGIN 120

Instructor: Yano

Production Systems Analysis: Read Less [-]

IND ENG 151 Service Operations Design and Analysis 3 Units

Terms offered: Fall 2022, Fall 2021, Fall 2020 This course is concerned with improving processes and designing facilities for service businesses such as banks, health care organizations, telephone call centers, restaurants, and transportation providers. Major topics in the course include design of service processes, layout and location of service facilities, demand forecasting, demand management, employee scheduling, service quality management, and capacity planning. Service Operations Design and Analysis: Read More [+]

Prerequisites: IND ENG 162 , IND ENG 173 , and a course in statistics

Service Operations Design and Analysis: Read Less [-]

IND ENG 153 Logistics Network Design and Supply Chain Management 3 Units

Terms offered: Spring 2024, Spring 2022, Fall 2021 We will focus primarily on both quantitative and qualitative issues which arise in the integrated design and management of the entire logistics network. Models and solution techniques for facility location and logistics network design will be considered. In addition, qualitative issues in distribution network structuring, centralized versus decentralized network control, variability in the supply chain, strategic partnerships, and product design for logistics will be considered through discussions and cases. Logistics Network Design and Supply Chain Management: Read More [+]

Prerequisites: IND ENG 160 , IND ENG 162 or senior standing

Instructor: Kaminsky

Logistics Network Design and Supply Chain Management: Read Less [-]

IND ENG 156 Healthcare Analytics 3 Units

Terms offered: Spring 2024 With the growing complexity of providing healthcare, it is increasingly important to design and manage health systems using engineering and analytics perspectives. This course will cover topics related to healthcare analytics, including: optimizing chronic disease management, designing matching markets for health systems, developing predictive analytics models, and managing resource utilization. Healthcare Analytics: Read More [+]

Prerequisites: Courses in mathematical modeling (such as IND ENG 160 and IND ENG 172 ) and computer programming (such as CS C8 or CS 61A) are recommended

Credit Restrictions: Students will receive no credit for IND ENG 156 after completing IND ENG 256 .

Instructor: Aswani

Healthcare Analytics: Read Less [-]

IND ENG 160 Nonlinear and Discrete Optimization 3 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This course introduces unconstrained and constrained optimization with continuous and discrete domains. Convex sets and convex functions; local optimality; KKT conditions; Lagrangian duality; steepest descent and Newton's method. Modeling with integer variables; branch-and-bound method; cutting planes. Models on production/inventory planning, logistics, portfolio optimization, factor modeling, classification with support vector machines. Nonlinear and Discrete Optimization: Read More [+]

Prerequisites: MATH 53 and MATH 54

Additional Format: Two hours of Lecture and One hour of Discussion per week for 15 weeks.

Instructor: Atamturk

Nonlinear and Discrete Optimization: Read Less [-]

IND ENG 162 Linear Programming and Network Flows 3 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This course addresses modeling and algorithms for optimization of linear constrained optimization problems. The simplex method; theorems of duality; complementary slackness. Applications in production planning and resource allocation. Graph and network problems as linear programs with integer solutions. Algorithms for selected network flow problems. Transportation and logistics problems. Dynamic programming and its role in applications to shortest paths, project management and equipment replacement. Linear Programming and Network Flows: Read More [+]

Instructor: Hochbaum

Linear Programming and Network Flows: Read Less [-]

IND ENG 164 Introduction to Optimization Modeling 3 Units

Terms offered: Spring 2024 Designed for students from any science/engineering major, this upper-division course will introduce students to optimization models, and train them to use software tools to model and solve optimization problems. The main goal is to develop proficiency in common optimization modeling languages, and learn how to integrate them with underlying optimization solvers. Students will work primarily on modeling exercises, which will develop confidence in modeling and solve optimization methods using software packages, and will require some programming. Review of linear and nonlinear optimization models, including optimization problems with discrete decision variables. Applications to practical problems from engineering and data science. Introduction to Optimization Modeling: Read More [+]

Course Objectives: • To introduce students to the core concepts of optimization • To train them in the art and science of using software tools to model and solve optimization problems.

Prerequisites: No prerequisites except some Python programming skills, which can be met by COMPSCI C8 (or any other Python-based course)

Additional Format: Two hours of lecture and one hour of discussion per week.

Introduction to Optimization Modeling: Read Less [-]

IND ENG 165 Engineering Statistics, Quality Control, and Forecasting 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course will introduce students to basic statistical techniques such as parameter estimation, hypothesis testing, regression analysis, analysis of variance. Applications in forecasting and quality control. Engineering Statistics, Quality Control, and Forecasting: Read More [+]

Prerequisites: IND ENG 172 , or STAT 134 , or an equivalent course in probability theory

Credit Restrictions: Students will receive no credit for IND ENG 165 after completing STAT 135 .

Summer: 6 weeks - 7.5 hours of lecture and 2.5 hours of discussion per week

Additional Format: Three hours of lecture and one hour of discussion per week. Seven and one-half hours of lecture and two and one-half hours of discussion per week for 6 weeks.

Engineering Statistics, Quality Control, and Forecasting: Read Less [-]

IND ENG 166 Decision Analytics 3 Units

Terms offered: Fall 2023, Spring 2022, Spring 2021 Introductory course on the theory and applications of decision analysis. Elective course that provides a systematic evaluation of decision-making problems under uncertainty. Emphasis on the formulation, analysis, and use of decision-making techniques in engineering, operations research and systems analysis. Includes formulation of risk problems and probabilistic risk assessments. Graphical methods and computer software using event trees, decision trees, and influence diagrams that focus on model design. Decision Analytics: Read More [+]

Prerequisites: IND ENG 172 or STAT 134

Instructors: Oren, Righter

Decision Analytics: Read Less [-]

IND ENG 169 Integer Optimization 3 Units

Terms offered: Spring 2022, Spring 2021, Fall 2020 This course addresses modeling and algorithms for integer programming problems, which are constrained optimization problems with integer-valued variables. Flexibility of integer optimization formulations; if-then constraints, fixed-costs, etc. Branch and Bound; Cutting plane methods; polyhedral theory. Applications in production planning, resource allocation, power generation, network design. Alternate formulations for integer optimization: strength of Linear Programming relaxations. Algorithms for integer optimization problems. Specialized strategies by integer programming solvers. Integer Optimization: Read More [+]

Course Objectives: • Enable the students to recognize when problems can be modeled as integer optimization problems. • Familiarize students in leading methodologies for solving integer optimization problems, and techniques in these methodologies. • To acquire skills in the best modeling approach that is suitable to the practical problem at hand. • To train students in modeling of integer optimization problems; • To train the students in the selection of appropriate techniques to be used for integer optimization problems.

Prerequisites: MATH 53 , MATH 54 , and background in Python and programming

Instructor: Rajan

Integer Optimization: Read Less [-]

IND ENG 170 Industrial Design and Human Factors 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This course surveys topics related to the design of products and interfaces ranging from alarm clocks, cell phones, and dashboards to logos, presentations, and web sites. Design of such systems requires familiarity with human factors and ergonomics, including the physics and perception of color, sound, and touch, as well as familiarity with case studies and contemporary practices in interface design and usability testing. Students will solve a series of design problems individually and in teams. Industrial Design and Human Factors: Read More [+]

Industrial Design and Human Factors: Read Less [-]

IND ENG 171 Berkeley Changemaker: Ethical and Effective Entrepreneurship in High Tech 3 Units

Terms offered: Fall 2024, Fall 2023, Spring 2023 This course emphasizes the three Berkeley Changemaker pillars of critical thinking, effective communication, and productive collaboration. It combines critical examination of entrepreneurial challenges with strategic, ethical, and leadership theories. It develops verbal and collaborative leadership skills, through flipped classroom and intense case discussions, a team project, and a formal final presentation of the project. The case discussions in particular will develop effective listening, real-time analysis, and verbal leadership skills. The project will challenge you to analyze a current or historical ethical challenge in a high technology industry, or analyze the ethical implications of your own entrepreneurial plans. Berkeley Changemaker: Ethical and Effective Entrepreneurship in High Tech: Read More [+]

Student Learning Outcomes: Students who fully engage with this class will strengthen their in-the-moment abilities to listen, learn, analyze, and convince. They will size up high tech business and entrepreneurial opportunities with new perspectives, both strategic and ethical. They will gain practice in applying strategic and ethical frameworks to entrepreneurship and business decisions in high technology. They will learn how to understand and build upon criticism in real-time, and lead discussions on contentious issues towards productive, inclusive, and mutually beneficial outcomes. They will become an entrepreneur who not only sees how innovation can solve society’s problems, but can furthermore convince and lead others in accomplishing and implementing a solution.

Credit Restrictions: Students will receive no credit for IND ENG 171 after completing UGBA 105 .

Summer: 8 weeks - 6 hours of lecture per week

Additional Format: Three hours of lecture per week. Six hours of lecture per week for 8 weeks.

Instructor: Fleming

Berkeley Changemaker: Ethical and Effective Entrepreneurship in High Tech: Read Less [-]

IND ENG 172 Probability and Risk Analysis for Engineers 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 This is an introductory course in probability designed to develop a good understanding of uncertain phenomena and the mathematical tools used to model and analyze it. Applications will be given in such areas as reliability theory, risk theory, inventory theory, financial models, and computer science, among others. This course is a probability course and cannot be used to fulfill any engineering unit or elective requirements. Probability and Risk Analysis for Engineers: Read More [+]

Course Objectives: Students will learn how to model random phenomena and learn about a variety of areas where it is important to estimate the likelihood of uncertain events. Students will also learn how to use computer simulation to replicate and analyze these events.

Prerequisites: MATH 1A , MATH 1B , and MATH 53

Credit Restrictions: Students will receive no credit for IND ENG 172 after completing STAT 134 , or STAT C140 .

Probability and Risk Analysis for Engineers: Read Less [-]

IND ENG 173 Introduction to Stochastic Processes 3 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 This is an introductory course in stochastic models. It builds upon a basic course in probability theory and extends the concept of a single random variable into collections of random variables known as stochastic processes. The course focuses on discrete-time Markov chains, Poisson process, continuous-time Markov chains, and renewal theory. It also discusses applications to queueing theory, risk analysis and reliability theory. Along with the theory, the course covers stochastic simulation techniques that will allow students to go beyond the models and applications discussed in the course. Introduction to Stochastic Processes: Read More [+]

Course Objectives: Students will learn how to model random phenomena that evolves over time, as well as the simulation techniques that enable the replication of such problems using a computer. By discussing various applications in science and engineering, students will be able to model many real world problems where uncertainty plays an important role.

Prerequisites: Students should have taken a probability course, such as STAT 134 or IND ENG 172 , and should have programming experience in Matlab or Python

Credit Restrictions: Students will receive no credit for Ind Eng 173 after taking Ind Eng 161.

Fall and/or spring: 15 weeks - 2 hours of lecture and 2 hours of discussion per week

Additional Format: Two hours of lecture and two hours of discussion per week.

Introduction to Stochastic Processes: Read Less [-]

IND ENG 174 Simulation for Enterprise-Scale Systems 3 Units

Terms offered: Fall 2024, Fall 2023, Fall 2022 Introductory course on design, programming, and statistical analysis of simulation methods and tools for enterprise-scale systems such as traffic and computer networks, health-care and financial systems, and factories. Topics include the types of problems that can be solved by such methods. Programming material includes the theory behind random variable generation for a variety of common variables. Advanced techniques such as variance reduction , simulation optimization, or meta-modeling are considered. Student teams implement an enterprise-scale simulation in a semester-length design project. Simulation for Enterprise-Scale Systems: Read More [+]

Course Objectives: • Exposure students to state-of-art advanced simulation techniques. • Note: the course is a mixture of modeling art, analytical science, and computational technology. • Have students communicate their ideas and solutions effectively in written reports. • Insure students become familiar with the fundamental similarities and differences among simulation software packages. • Introduce students to modern techniques for developing computer simulations of stochastic discrete-event models and experimenting with such models to better design and operate dynamic systems. • Introduce the different technologies used to develop simulation models and simulator products in order to become critical consumers of simulation study results. • Teach strengths and weaknesses of different approaches for a foundation for selecting methodologies. • Teach students how to model random processes and experiment with simulated systems.

Prerequisites: IND ENG 165 ; IND ENG 173 ; IND ENG 172 or STAT 134

Credit Restrictions: Students will receive no credit for IND ENG 174 after completing IND ENG 131. A deficient grade in IND ENG 174 may be removed by taking IND ENG 131.

Instructor: Zheng

Simulation for Enterprise-Scale Systems: Read Less [-]

IND ENG 180 Senior Project 4 Units

Terms offered: Spring 2024, Spring 2023, Spring 2022 Application of systems analysis and industrial engineering to the analysis, planning, and/or design of industrial, service, and government systems. Consideration of technical and economic aspects of equipment and process design. Students work in teams under faculty supervision. Topics vary yearly. Senior Project: Read More [+]

Prerequisites: 160, 162, 165, 173, Engineering 120, and three other Industrial Engineering and Operations Research electives

Fall and/or spring: 15 weeks - 2 hours of lecture and 6 hours of fieldwork per week

Summer: 10 weeks - 3 hours of lecture and 9 hours of fieldwork per week

Additional Format: Two hours of lecture and six hours of fieldwork per week. Three hours of lecture and nine hours of fieldwork per week for 10 weeks.

Grading/Final exam status: Letter grade. Final exam not required.

Senior Project: Read Less [-]

IND ENG 190B Advanced Topics in Industrial Engineering and Operations Research: Entrepreneurial Marketing and Finance 1 - 4 Units

Terms offered: Fall 2017, Spring 2014, Fall 2013 The 190 series cannot be used to fulfill any engineering requirement (engineering units, courses, technical electives, or otherwise). Advanced Topics in Industrial Engineering and Operations Research: Entrepreneurial Marketing and Finance: Read More [+]

Fall and/or spring: 15 weeks - 1-4 hours of seminar per week

Summer: 8 weeks - 1.5-7.5 hours of seminar per week 10 weeks - 1.5-6 hours of seminar per week

Additional Format: One to Four hour of Seminar per week for 15 weeks. One and one-half to Six hours of Seminar per week for 10 weeks. One and one-half to Seven and one-half hours of Seminar per week for 8 weeks.

Advanced Topics in Industrial Engineering and Operations Research: Entrepreneurial Marketing and Finance: Read Less [-]

IND ENG 190C Advanced Topics in Industrial Engineering and Operations Research 1 - 4 Units

Terms offered: Spring 2020, Fall 2019, Spring 2019 The 190 series cannot be used to fulfill any engineering requirement (engineering units, courses, technical electives, or otherwise). Advanced Topics in Industrial Engineering and Operations Research: Read More [+]

Advanced Topics in Industrial Engineering and Operations Research: Read Less [-]

IND ENG 190D Advanced Topics in Industrial Engineering and Operations Research 1 - 4 Units

Terms offered: Spring 2017, Fall 2014, Spring 2014 The 190 series cannot be used to fulfill any engineering requirement (engineering units, courses, technical electives, or otherwise). Advanced Topics in Industrial Engineering and Operations Research: Read More [+]

IND ENG 190F Advanced Topics in Industrial Engineering and Operations Research 1 - 4 Units

Terms offered: Spring 2013, Spring 2012, Spring 2011 The 190 series cannot be used to fulfill any engineering requirement (engineering units, courses, technical electives, or otherwise). Advanced Topics in Industrial Engineering and Operations Research: Read More [+]

IND ENG 190G Advanced Topics in Industrial Engineering and Operations Research 1 - 4 Units

Ind eng 190h cases in global innovation 1 unit.

Terms offered: Fall 2021, Spring 2011 This course is designed primarily for upper-level undergraduate and graduate students interested in examining the major challenges and success factors entrepreneurs and innovators face in globalizing a company, product, or service. Over the duration of this course, students will examines case studies of early, mid-stage, and large-scale enterprises as they seek to start a new venture, introduce a new product or service, or capitalize on global economic trends to enhance their existing business. The course content exposes students interested in internationally oriented careers to the strategic thinking involved in international engagement and expansion. Cases will include both U.S. companies seeking to enter emerging markets and emerging market companies looking to expand within their own nations or into markets in developed nations. The course is focused around intensive study of actual business situations through rigorous case-study analysis. Cases in Global Innovation: Read More [+]

Prerequisites: Junior or Senior standing

Fall and/or spring: 8 weeks - 2 hours of lecture per week

Additional Format: Two hours of lecture per week for eight weeks.

Cases in Global Innovation: Read Less [-]

IND ENG 190I Cases in Global Innovation: China 1 Unit

Terms offered: Prior to 2007 This course is designed primarily for upper-level undergraduate and graduate students interested in examining the major challenges and success factors entrepreneurs and innovators face in globalizing a company product or service, with a focus on China. Over the duration of this course, students will examine case studies of foreign companies seeking to start a new venture, introduce a new product or service to the China market, or domestic Chinese companies seeking to adapt a U.S. or western business model to the China market. The course content exposes students interested in internationally oriented careers to the strategic thinking involved in international engagement and expansion and the particularities of the China market and their contrast with the U.S. market. The course is focused around intensive study of actual business situations through rigorous case-study analysis and the course size is limited to 30. Cases in Global Innovation: China: Read More [+]

Prerequisites: Junior or senior standing. Recommended, but not required to be taken after or along with Engineering 198

Fall and/or spring: 15 weeks - 2 hours of lecture per week

Cases in Global Innovation: China: Read Less [-]

IND ENG 190K Cases in Global Innovation: South Asia 1 Unit

Terms offered: Prior to 2007 This course is designed primarily for upper-level undergraduate and graduate students interested in examining the major challenges and success factors entrepreneurs and innovators face in conducting business, globalizing a company product or service, or investing in South Asia. Over the duration of this course, students will examine case studies of foreign companies seeking to start a new venture, introduce a new product or service to the South Asian market, or South Asian companies seeking to adapt a U.S or western business model. The course will put this into the larger context of the political, economic, and social climate in several South Asian countries and explore the constraints to doing business, as well as the policy changes that have allowed for a more conducive business environment. Cases in Global Innovation: South Asia: Read More [+]

Prerequisites: Junior or senior standing. Recommended but not required to be taken after or along with Engineering 198

Cases in Global Innovation: South Asia: Read Less [-]

IND ENG H196A Operations Research and Management Science Honors Thesis 3 Units

Terms offered: Fall 2022 Individual study and research for at least one academic year on a special problem approved by a member of the faculty; preparation of the thesis on broader aspects of this work. Operations Research and Management Science Honors Thesis: Read More [+]

Prerequisites: Open only to students in the honors program

Credit Restrictions: Course may be repeated for credit with consent of instructor.

Repeat rules: Course may be repeated for credit with instructor consent.

Fall and/or spring: 15 weeks - 3 hours of independent study per week

Additional Format: Three hours of Independent study per week for 15 weeks.

Grading/Final exam status: Offered for pass/not pass grade only. Final exam required.

Operations Research and Management Science Honors Thesis: Read Less [-]

IND ENG H196B Operations Research and Management Science Honors Thesis 3 Units

Terms offered: Prior to 2007 Individual study and research for at least one academic year on a special problem approved by a member of the faculty; preparation of the thesis on broader aspects of this work. Operations Research and Management Science Honors Thesis: Read More [+]

IND ENG 197 Undergraduate Field Research in Industrial Engineering 1 - 12 Units

Terms offered: Spring 2023, Fall 2022, Spring 2022 Students work on a field project under the supervision of a faculty member. Course does not satisfy unit or residence requirements for bachelor's degree. Undergraduate Field Research in Industrial Engineering: Read More [+]

Prerequisites: Completion of two semesters of coursework

Fall and/or spring: 15 weeks - 1-12 hours of fieldwork per week

Summer: 6 weeks - 2.5-30 hours of fieldwork per week 8 weeks - 1.5-22.5 hours of fieldwork per week 10 weeks - 1.5-18 hours of fieldwork per week

Additional Format: Forty-five hours of academic work per unit per term. Forty-five hours of academic work per unit per term.

Undergraduate Field Research in Industrial Engineering: Read Less [-]

IND ENG 198 Directed Group Studies for Advanced Undergraduates 1 - 4 Units

Terms offered: Fall 2024, Spring 2024, Fall 2023 Group studies of selected topics. Semester course unit value and contact hours will have a one-to-one ratio. Directed Group Studies for Advanced Undergraduates: Read More [+]

Prerequisites: Senior standing in Engineering

Fall and/or spring: 15 weeks - 1-4 hours of directed group study per week

Additional Format: One to Four hour of Directed group study per week for 15 weeks.

Directed Group Studies for Advanced Undergraduates: Read Less [-]

IND ENG 199 Supervised Independent Study 1 - 4 Units

Terms offered: Fall 2022, Fall 2021, Fall 2020 Supervised independent study. Enrollment restrictions apply. Supervised Independent Study: Read More [+]

Prerequisites: Consent of instructor and major adviser

Credit Restrictions: Course may be repeated for a maximum of four units per semester.

Summer: 6 weeks - 2.5-10 hours of independent study per week 8 weeks - 2-7.5 hours of independent study per week 10 weeks - 1.5-6 hours of independent study per week

Additional Format: One to four hours of independent study per week. One and one-half to six hours of independent study per week for 10 weeks. Two to seven and one-half hours of independent study per week for 8 weeks. Two and one-half to ten hours of independent study per week for 6 weeks.

Supervised Independent Study: Read Less [-]

Contact Information

Department of industrial engineering and operations research.

4141 Etcheverry Hall

Phone: 510-642-5484

Professor and Chair

Alper Atamturk

423 Sutardja Dai Hall

Phone: 510-642-4559

[email protected]

Student Affairs Officer

Ginnie Sadil

[email protected]

ORMS Head Undergraduate Advisor

Dorit Hochbaum

4181 Etcheverry Hall

Phone: 510-642-4998

[email protected]

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May 7, 2024 Bendoly recognized for top-10 research

A paper co-authored by Fisher’s Elliot Bendoly was recently recognized by a leading journal as one of its most impactful pieces of research published in decades.

Bendoly, a Distinguished Professor of Operations and Business Analytics at Fisher, co-wrote “Bodies of Knowledge for Research in Behavioral Operations.” The paper was named one of the top 10 pieces published in Production and Operations Management (POMS) in the last 30 years.

The research examines the role behavioral sciences such as cognitive psychology, social psychology, group dynamics and system dynamics can have on operations management research. The paper sought to lower startup costs for new researchers within the behavioral operations field.

Bendoly co-authored the piece in 2010 with Rachel T.A. Croson, executive vice president and provost at the University of Minnesota; Kenneth Schultz, associate professor at the Air Force Institute of Technology; and Paulo Goncalves, professor at the University of Lugano.

In addition to his research, Bendoly has distinguished himself as an operations thought leader through his service to the academic community. In January 2024, he was named the editor-in-chief of the Journal of Operations Management  and has been honored as an OM Distinguished Scholar by the Academy of Management.

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