UNDERGRAD SUPPLY OP-ED: ANTI-RACIST BUSINESS …

OP-ED: ANTI-RACIST TEACHING PRACTICESValuing students and their motivations

QUANTUM COMPUTING IN OR/MS COURSESStudents need QC skills for job market

August 2021 | Volume 48, Number 4

BUSINESS ANALYTICS BEST PRACTICESUnderstanding taxonomy eases teaching analytics

HISTORY OF O.R. AT STANFORD67-year eyewitness account of core program

UNDERGRAD SUPPLY CHAIN COURSESIncreasing SCRM content and approaches

fliPping Innovative Education Issue

the classroomRemote learning best practices

Also inside: Tippie’s journey to the Smith Prize

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The 2021 INFORMS Annual Meeting, held October 24–27, is a unique

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EARLY BIRD DEADLINE: SEPTEMBER 27 meetings.informs.org/anaheim2021

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2 ORMSTODAY.INFORMS.ORG I AUGUST 2021

24FLIPPING THE CLASSROOM Student response to remote learning classroom model at Northwestern.

BY ELOISE CHUDIK, JILL HARDIN WILSON, DAVID MORTON AND EOJIN HAN

DepartmentsInside Story

President's Desk

HQ Highlights

INFORMS Online

Advocacy in D.C.

Forum

Issues in Education

INFORMS Prizes

International O.R.

Member Insights

Student Perspectives

INFORMS in Action

BOD Candidate Statements

Conference Preview

Last Word

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44QUANTUM COMPUTING IN OR/MS EDUCATIONThe importance of weaving QC courses into OR/MS programs.

BY GIACOMO NANNICINI, SWATI GUPTA, SVEN LEYFFER, JIM OSTROWSKI AND LUIS F. ZULUAGA

38BETTER PRACTICES FOR TEACHING BUSINESS ANALYTICSUnderstanding the taxonomy of “analytics” can boost academic programs and course offerings.

BY DURSUN DELEN

34OP-ED: ANTI-RACIST TEACHING PRACTICES One instructor’s ongoing journey to help dismantle racism by empowering underrepresented students in the classroom.

BY SOMMER GENTRY

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AUGUST 2021 CONTENTS

50HISTORY OF O.R. AT STANFORD UNIVERSITY 67-year eyewitness account of the formidable O.R. department producing major contributors to operations research.

BY FREDERICK S. HILLIER

30UNDERGRADUATE SUPPLY CHAIN CURRICULUMTime to include more supply chain risk management content in intro SCM courses.

BY SAJAD EBRAHIMI

& Awards


EDITORS OF OTHER INFORMS PUBLICATIONSDecision Analysis Vicki M. Bier, University of Wisconsin-Madison Information Systems Research Alok Gupta, University of MinnesotaINFORMS Analytics Collections Nicholas G. Hall, The Ohio State UniversityINFORMS Journal on Applied Analytics Michael F. Gorman, University of DaytonINFORMS Journal on Computing Alice E. Smith, Auburn University INFORMS Journal on Data ScienceGalit Shmueli, National Tsing Hua University INFORMS Journal on Optimization Dimitris Bertsimas, Massachusetts Institute of TechnologyINFORMS Transactions on Education Jeroen Belien, KU LeuvenManagement Science David Simchi-Levi, Massachusetts Institute of TechnologyManufacturing & Service Operations Management Georgia Perakis, Massachusetts Institute of TechnologyMarketing ScienceK. Sudhir, Yale UniversityMathematics of Operations ResearchKatya Scheinberg, Cornell UniversityOperations Research John R. Birge, University of ChicagoOrganization Science Gautam Ahuja, Cornell UniversityService Science Saif Benjaafar, University of MinnesotaStrategy Science Daniel A. Levinthal, University of PennsylvaniaTransportation Science Karen Smilowitz, Northwestern UniversityTutORials in Operations Research Douglas Shier, Clemson University

OR/MS TODAY (ISSN 1085-1038) is published bimonthly by the Institute for Operations Research and the Management Sciences (INFORMS). Canada Post International Publications Mail (Canadian Distribution) Sales Agreement No. 1220047. Deadlines for contributions: Manuscripts and news items should arrive no later than six weeks prior to the first day of the month of publication. Address correspondence regarding editorial content to the editor, Kara Tucker: email: [email protected]; phone: 443-757-3572. The opinions expressed in OR/MS Today are those of the authors, and do not necessarily reflect the opinions of INFORMS, its officers or the editorial staff of OR/MS Today. Membership subscriptions for OR/MS Today are included in annual dues. INFORMS offers non-member subscriptions to institutions, the rate is $62 USA, $79 Canada & Mexico and $85 all other countries. Single copies can be purchased for $25 plus postage. Periodicals postage paid at Catonsville, MD, and additional mailing offices. Printed in the United States of America. POSTMASTER: Send address changes to OR/MS Today, INFORMS, 5521 Research Park Dr., Suite 200, Catonsville, MD 21228. OR/MS Today ©2021 by the Institute for Operations Research and the Management Sciences. All rights reserved.

OR/MS TODAY ADVERTISING AND EDITORIAL OFFICE5521 Research Park Drive, Suite 200, Catonsville, MD 21228 Tel.: 443.757.3500Email: [email protected]

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Advertising SalesOlivia Schmitz, [email protected]

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Magazine Editorial Advisory Board James Cochran, Committee Chair

INFORMS Onlinewww.informs.orgormstoday.informs.org

INFORMS BOARD OF DIRECTORSPresident Stephen Graves, Massachusetts Institute of TechnologyPresident-Elect Radhika Kulkarni, SAS Institute Inc., RetiredPast President Pinar Keskinocak, Georgia TechSecretary Victoria Chen, University of Texas at Arlington Treasurer David Hunt, Oliver WymanVice President, Meetings Tamas Terlaky, Lehigh University Vice President, Publications Cole Smith, Syracuse UniversityVice President, Sections and Societies Maciek Nowak, Loyola UniversityVice President, Technology Strategy Subramanian Raghavan, University of MarylandVice President, Practice C. Allen Butler, Daniel H. Wagner AssociatesVice President, International Activities Sue Merchant, Blue Link ConsultingVice President, Membership and Professional Recognition Pelin Pekgun, University of South Carolina Vice President, EducationMelissa Bowers, University of Tennessee, KnoxvilleVice President, Marketing, Communications and Outreach L. Beril Toktay, Georgia TechVice President, Chapters and Fora Trevor Bihl, Air Force Research Laboratory Executive DirectorElena Gerstmann, Ph.D.

OR/MS TODAY

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6 ORMSTODAY.INFORMS.ORG I AUGUST 2021 7ORMSTODAY.INFORMS.ORG I AUGUST 2021

INSIDE STORY

KEEPING COURSE MATERIAL FRESH AND INSTRUCTORS ‘WOKE’BY KARA TUCKER

AS COLLEGES AND UNIVERSITIES PREPARE to welcome students back to campus for the 2021- 2022 academic year, I am delighted to present the annual innovative education special issue of OR/MS Today. Last year’s special issue came at a time when classrooms were in complete flux due to the COVID-19 pandemic. Most of the issue was based on teachers’ experiences switching to online learning – once they were able to come up for air. This year, it looks like most campuses will fully reopen again for in-person classes, although the virus and its impact continue to change daily. I myself am wondering what the fall will look like for my elementary-age children. Masks, I hope? Will social distancing remain?

Only a few of the offerings in this year’s special issue mention COVID classrooms, including an article by some Northwestern University faculty and alum. “The Flipped Classroom” outlines the authors’ process “flipping” a probability course from in-person to online. They share what worked, what didn’t, and how students reacted to this new normal (page 24).

However, now that most instructors have mastered synchronous learning and telling students “You’re on mute,” we decided to move beyond the “how your class can survive a pandemic” articles – everyone has passed that test with flying colors.

Giacomo Nannicini et al. and Sajad Ebrahimi detail course curricula in quantum computing and supply chain management, respectively (pages 44 and 30). Both articles are first-hand experiences and provide helpful insights and best practices for incorporating tried-and-true course material into analytics programs. Analytics, as we learn from

EXHIBIT & SPONSORA Flexible Conference: In Person & Virtual

Join us for the 2021 INFORMS Annual Meeting, October 24–27. Participate either in person in Anaheim, CA or virtually via our online meeting platform to connect with 5,000+ in-person & virtual students, professionals, academics, industry leaders, and government representatives to share ideas and research, and explore best practices.

Meeting at a Glance

• 5,000-7,000 In-person & Virtual Attendees

• 85% Master’s or PhDs

For more information on exhibit and sponsor opportunities, please visit: meetings.informs.org/anaheim2021/exhibit-sponsor

• 40% Student Attendance

• 85% Academic Audience

Dursun Delen’s article “Better Practices for Teaching Business Analytics,” is just one of many terms an institution might use to describe its undergraduate and graduate analytics programs or certificates. Only after understanding the nuances and taxonomy of “analytics” and “data science” can academic institutions, and subsequently professors, provide the appropriate courses and content for students to be fully prepared for the continually growing analytics workforce (see page 38).

With plenty of advice for curriculum content and better teaching practices, Sommer Gentry’s “best practices” advice takes instructors through a much different journey. In her article, “Anti-racist teaching practices,” Gentry describes practices she has adopted as a mathematics professor at an undergraduate institution in pursuit of the immense goal to dismantle racism, including ways to make classrooms locations of empowerment and progress for underrepresented minority students. The main takeaway? Anti-racist teaching is simply good teaching. Read the op-ed on page 34.

What’s education without a history lesson? Fred Hillier is back with his 67-year eyewitness account of operations research at Stanford University (page 50). The O.R. department changed plenty over the decades, but what remains constant is the renowned faculty and graduates who majorly contribute to the field of O.R. (George Dantzig, Harvey Wagner, Kenneth Arrow, Al Roth – the list goes on).

Beyond the featured articles, there are several columns that touch on innovative education as well, including the full story about the Tippie College of

Business’ journey to winning the UPS Smith Prize – awarded for effective and innovative preparation of students to be good practitioners of OR/MS or analytics (page 18). Bryan Bell, chief data scientist at Aviso Retention, discusses the “summer melt,” and how predictive and prescriptive analytics can help fight college attrition (page 14).

This issue also features possibly our first-ever recent high school graduate author, who wrote (with her father) an international O.R. story about optimization in her high school in Italy (page 20). (We’re happy to report she recently earned her scientific high school diploma from Liceo Scientifico “Leonardo da Vinci” in Crema, Italy, with full marks and cum laude.)

Speaking of first-ever, check out the 2021 Annual Meeting preview on page 64 that describes the nature of the first hybrid event at INFORMS and what attendees – both in-person and virtual – can expect. We’re also rolling out a new column titled “Member Insights.” These short stories will be – you guessed it – written by INFORMS members sharing personal advice to help other members make decisions about their education and career. The first installation, by Wendy Swenson-Roth, provides a full description of member benefit and online tool, the INFORMS Academic Program Database (page 56).

What follows is the student perspective of this database by two members of the OR/MS Tomorrow editorial team. Mihir Mehta and Abigail Lindner explain how to navigate the database and its functions to help students and early career professionals identify analytics programs that best fit their interests and career goals.

Part of what makes being an academic so thrilling is conducting research that can benefit society, and publishing that research is a big part of providing its impact. Many INFORMS members publish in INFORMS’ journals to promote OR/ MS and analytics research, and many have asked, “Why does it take so long for my article to publish?” INFORMS Director of Publications Matt Walls and Vice President of Publications Cole Smith respond to this query and more in, “Challenges Facing INFORMS Journals: The Way Forward” (page 60). In sum, “INFORMS understands that its ability to meet the challenges of refereeing and publishing this unprecedented volume of journal papers will in no small part determine our ability to remain at the forefront of disseminating the most significant operations research, management science and analytics studies in the coming decade.” Stick with us.

KARA TUCKER ([email protected]) is editor of OR/MS Today.


PRESIDENT’S DESK

DIVERSITY, EQUITY AND INCLUSION STANDARDS AT INFORMSHow can INFORMS best achieve its goal to advance DEI in all we do?BY STEVE GRAVES

THE JUNE 2021 ISSUE OF OR/MS TODAY had an unintentional DEI theme with columns and articles speaking to INFORMS’ strategic goal: “Advance diversity, equity and inclusion in all we do.” I expect we’ll see more DEI content sprinkled throughout future issues as well. In this column, I’ll add my perspectives and prescriptions on this.

How Do We Advance Inclusion? INFORMS should not only welcome all to join our community and engage in our activities, but should assure that all feel welcome doing so. We accomplish this by creating and maintaining an environment and culture that respects all, values everyone’s contributions and inputs, and encourages inclusivity. Much of this relates to how we do the “business” of a professional association: How we select volunteers with whom to work; how we communicate and interact with each other; how we make decisions and choices; and how we recognize and reward accomplishments and contributions.

To help create this inclusive community environment and culture, INFORMS is updating the INFORMS Code of Conduct [1] as a way of setting expectations and norms. More details on this will come later in the year, but the essence of the code remains that “... all participants conduct themselves during INFORMS activities in a professional manner that is welcoming to all participants and free from any

form of discrimination, harassment, intimidation or retaliation. Participants agree to treat each other with respect and consideration to create and ensure a collegial, inclusive and professional environment.” The Code also includes the processes for reporting and dealing with bad behavior, which will be fleshed out in the updated Code.

In addition, INFORMS needs to continue to monitor our culture and environment and determine if we are making progress in helping all individuals feel welcome within INFORMS. One way to do this is to regularly survey our membership and ask about their experiences. One example from within INFORMS is the MSOM Society, which provides a great model for this practice. Since 2019, the MSOM Society has annually surveyed its membership on any issues or occurrences of discrimination, exclusion or harassment at MSOM-sponsored events. Importantly, the survey results are then presented and discussed at an MSOM business meeting. I know there are plans to replicate, in some fashion, this practice with other subdivisions, as well as within INFORMS as a whole.

How Do We Advance Equity?INFORMS does this best through research and applications that address societal inequities, wherever and however they arise. Our mission is to advance and promote the science and technology of

decision-making to save lives, save money and solve problems. Increasingly, the INFORMS community has equated “save lives, save money and solve problems” to “reduce inequity.” Over time, INFORMS has committed more and more of its energy to “doing good with good O.R. and analytics.”

It is not hard to find many outstanding examples of “doing good with O.R. and analytics.” In the past year, Management Science published, as a virtual special issue, a collection of recent papers from the journal related to issues of diversity, equity and inclusion [2]. The research examines discrimination, biases and inequities in a range of contexts including housing, healthcare and hiring.

The 2021 INFORMS Edelman Award winner is another impressive example. The award was given to the U.N. World Food Programme (WFP) for its innovative and impactful applications of O.R. and analytics to its humanitarian operations, which saved money and lives [3]. WFP credits these efforts with saving $150 million that allows it to deliver enough food to feed 2 million people for a year.

INFORMS Healthcare Conference 2021 showcased great work that has been done by the INFORMS community to fight the COVID-19 pandemic, with more than 100 sessions on the pandemic, many addressing how to reduce the inequities that arose during the pandemic in resource allocation and healthcare delivery.

I could go on with more examples of how the work of INFORMS members advances equity, and we should highlight this important work. Yet, in light of the prominence and persistence of societal inequity, we must strive to do even more. We need to think more about how to encourage and create opportunities to “solve problems” that reduce inequity. For instance, consider the INFORMS Pro Bono Analytics program that brings expertise in O.R. and analytics to nonprofits for some societal good; this is an outstanding program, but could it be an order of magnitude larger? We might also advocate for funding from foundations and government to support research to solve problems of inequity in particular contexts. Finally, we could do more to recognize and celebrate research and applications that reduce inequities, for instance with prize competitions similar to the Edelman Award and Wagner Prize.

How Do We Advance Diversity?Over my career, INFORMS has become diverse across many dimensions and continues to do a better job at valuing this diversity and benefiting from it in all INFORMS activities. Yet INFORMS has very few underrepresented minority members, especially African Americans.

To increase the number of underrepresented minorities in our profession we have the most leverage by focusing on the pipeline – this starts with K-12 STEM education. INFORMS can and has advocated for more funding to improve K-12

STEM education [4]. INFORMS can also work more directly with schools to improve their curriculum. An exceptional example is the MIT BLOSSOMS initiative [5] created by Dick Larson (INFORMS past president and MIT colleague) and Elizabeth Murray. This initiative produces and distributes, at no cost, math and science video lessons for high school students, and now has a library of more than 150 video lessons that are in use worldwide. Furthermore, BLOSSOMS itself is extremely diverse and inclusive with 11 countries represented, many ethnicities, multiple religions and more than 50% female STEM presenters.

There are several other noteworthy examples focused on K-12 STEM education as part of the DEI Ambassadors program [6], such as the Seth Bonder Camp in Computational and Data Science for Engineering [7], which is run by Pascal Van Hentenryck at Georgia Tech, with a goal to increase the number of African Americans and Hispanics in data science. Quoting Dick Larson, “... I can’t think of a better profession than O.R. for bringing math to the real world for high school STEM students” [8].

Beyond K-12, we also need to continue to raise awareness of O.R. and analytics to college students and excite them about career prospects, as well as the opportunities from graduate education in O.R. and analytics. Ultimately, we need to increase the number of underrepresented students who have the educational foundation and the interest to join us at INFORMS.

Overall, there’s a lot to do. However, we are not starting from zero, and we do have momentum along several lines. To make progress on this strategic goal, I have suggested some themes for our efforts and investments. Please let me know what you think, at [email protected], as well as any other ideas and input.

STEVE GRAVES is the Abraham J. Siegel Professor of Management at the MIT Sloan School of Management. He is the 2021 INFORMS president.

REFERENCES AND NOTES1. Current Code of Conduct is available at: https://www.informs.org/

About-INFORMS/Governance/INFORMS-Code-of-Conduct. 2. https://pubsonline.informs.org/page/mnsc/papers-on-diversity-

equity-inclusion3. Tucker, K., 2021, “Analytics drives U.N. World Food Programme

response to operations, Edelman win,” OR/MS Today, June, pp 38-41.4. https://www.meritalk.com/articles/bipartisan-

bills-aim-to-strengthen-k-12-stem-education/5. https://blossoms.mit.edu/mit_blossoms_initiative_math_

science_video_lessons_high_school_students6. https://connect.informs.org/diversity/ambassador-program7. https://sethbondercamp.isye.gatech.edu/8. Personal communication, July 2021.

ADDRESSING CHALLENGES AND EMBRACING NEW OPPORTUNITIES

Check out a recent episode of the Resoundingly Human podcast featuring Steve Graves as he discusses progress and milestones and what the rest of 2021 holds for INFORMS and its members!


HQ HIGHLIGHTS

AN ANNIVERSARY LETTERBY ELENA GERSTMANN

Dear members,

“It was the best of times, it was the worst of times…”

I know I’m not Charles Dickens, but this is the phrase I think of when I describe my first year at INFORMS. Just think about it – I was hired during a worldwide pandemic! The interview process was entirely via Zoom. As of today, I still have met only one member of the INFORMS Board of Directors in person. I finally met most of the 55 staff members in July as we began the process of bringing staff back into our office in Catonsville, Md.

Face-to-face meetings and networking opportunities are the secret sauce of professional associations, and this evaporated over the past 18 months. At INFORMS, it vanished along with nearly 30% of our revenue and a significant decline in our membership. Moreover, our academic members have shared with me how stressful it was when they were required to teach online, juggle their research programs, and, of course, their own families while worrying about COVID-19. Our members from industry worried about their own jobs being cut (especially those who work in the travel industry) and having to lay off their employees. INFORMS’ own staff stressed about delivering the best experience to our members (you!) and customers from their home offices while juggling their own families and worrying whether INFORMS would need to lay off staff.

Thankfully, the INFORMS Board of Directors endorsed the use of our rainy-day funds to fully support our members and staff during this time. Access to these funds allowed INFORMS to provide hardship discounts for INFORMS members to attend events, keep member services levels the same, retain all in-house staff members, and many other things that are invisible to our membership but will set us up for a strong future.

Even against the background of these and other struggles, I was able to see the wonder of INFORMS. I watched many volunteer leaders in our subdivisions pivot from offering in-person events for their smaller, targeted groups to offering online events for the

world. I was amazed by the thousands of people who still submitted papers and participated in the INFORMS 2020 Annual Meeting, and the success of our Virtual 2021 Business Analytics Conference. I’ve seen the number of submissions to our journals and downloads of articles skyrocket. I watched incredible Edelman Award finalist competitions that showcased the amazing work being done throughout the world using O.R. and analytics. And, like you, I took great pride in the awards earned across many INFORMS competitions by the best, brightest, most committed individuals in our esteemed field.

I have been able to see the amazing work our members have been doing that truly saves lives, saves money and solves problems – from the global imperative of winning the fight against COVID-19 to tackling systemic racism. I met hundreds of members during one-on-one Zoom meetings. These individuals were generous with their time as they helped me onboard and learn about our great organization. Many shared stories with me about their first time at an INFORMS meeting or how they found multiple jobs through their INFORMS network, and about the lifelong friendships and professional collaborations that developed because of INFORMS; the list goes on. On each call, I learned and understood more and more about members like you, our organization, and how critical INFORMS is in the world and in people’s lives.

This really is a long-winded way of saying that as I celebrate my first year as executive director, I thank you for being a member and for being part of INFORMS’ future. I can’t wait to meet and work with more of you in the months and years ahead.

As always, if you ever have a great story to share with me or have feedback on how INFORMS can better serve you, my inbox is open.

Best regards,

ELENA GERSTMANN ([email protected]), Ph.D., FASAE, is the executive director of INFORMS (5521 Research Park Drive, Ste. 200, Catonsville, MD 21228). She can be reached via email or by phone at 443-757-3521.

J ULY 27, 2021

ELENA

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RESOUNDINGLY HUMAN June 2021

In these episodes we discuss how to ensure government programs stick to their budgets, explore new research on the impact of rideshares on reduce sexual assault rates in the surrounding community, hear an update from our 2021 INFORMS president, and learn one guest's exciting journey to working with the Philadelphia EaglesJune's episodes feature Dwaipayan Roy, Anant Mishra and Kingshuk Sinha, University of Minnesota; Jiyong Park, University of North Carolina at Greensboro; Stephen Graves, 2021 INFORMS president; and Zachary Steever, Philadelphia Eagles.

July 2021

These episodes discuss our changing airspace and the incredible possibilities introduced by unmanned aircraft, how to create a diverse and welcoming workspace for those living with disabilities, a data-based approach to improving election security, and an introduction to BlockPower and its impact on Black voter turnout.July's episodes feature John-Paul Clarke, The University of Texas at Austin; Dustin Cole, Michigan State University; Josh Dehlinger, Towson University; and Chris Parker, American University, and Karthik Balasubramanian, Howard University.

Check out the top 8 most-clicked items on INFORMS' social media channels last month. Connect with us on your favorite channel!

INFORMS ONLINE

Public health experts: Olympics can be held safely, if changes are made, The Washington Times, featuring Sheldon Jacobson

These car rental alternatives will get you there this summer, The Washington Post, featuring Sridhar Tayur

Sometimes it takes an academic eye to spot the flaws in a practical system, like federal IT acquisition, Federal News Network, featuring Dwaipayan Roy

Direct Flights Save Lives! New Airline Routes Can Increase Kidney Sharing by More than 7%

New Research Says Millions of Dollars Saved Daily When Scheduled Travel Providers Adapt to On-Demand Service Scheduling Based on Commuter and Traffic Patterns

INFORMS Endorses Bipartisan, Bicameral Legislation to Modernize STEM Education

Do "Made in USA" Claims Make a Difference in Marketing Results?

MEMBERS IN THE NEWS


ADVOCACY IN D.C.

INFORMS ON THE HILL AND IN THE PRESSBY JEFF COHEN

WHILE WASHINGTON, D.C., ALWAYS SEEMS TO be busy, the past few months have been particularly active for INFORMS and its members engaged across the federal policy ecosystem. We have partnered on innovative opportunities for operations research (O.R.) and analytics, launched some new initiatives and effectively engaged INFORMS members in a variety of key policy discussions. INFORMS continues to be excited about the progress we are making.

As you may recall, we structured this year’s advocacy activities around the idea of “21 for 21” (see my April 2021 OR/MS Today column [1]). This effort is successfully leveraging INFORMS members across a broad spectrum of relevant issues throughout the policy ecosystem. Recent areas of focus have included supply chains, COVID-19, artificial intelligence, food security, vaccine distribution, cybersecurity and election security, along with ongoing efforts to educate policymakers about O.R. and analytics more broadly.

Here are a few specific projects the INFORMS advocacy team is working on.

Mathematical and Statistical Modeling Education Act. This legislation would modernize science, technology, engineering and math (STEM) education in the United States to attract more young people into these studies and fields to enhance the nation’s pipeline of STEM talent. It was introduced in May on a bipartisan and bicameral basis by Senators Maggie Hassan and Marsha Blackburn in the U.S. Senate and Representatives Chrissy Houlahan and Jim Baird in the U.S. House of Representatives. (S. 1839 / H.R. 3588).

Importantly, INFORMS’ advocacy work on Capitol Hill informed aspects of the language in this bill [2], and the legislation specifically calls out operations research, suggesting it is a foundational element of data-driven decision-making.

National Science Foundation funding. President Biden recently sent his proposed budget to Congress, which officially begins the Fiscal Year 2022 federal funding process. This budget included $10.2 billion in funding for the National Science Foundation (NSF), which represents a 20% increase from the previous year. This significant increase will provide additional opportunities for needed research.

INFORMS has sent a letter to the U.S. House and Senate appropriations committees requesting that Congress fund the president’s proposed increase for the NSF. We are hopeful that INFORMS’ engagement on this issue will help to align our community’s research needs and objectives with funding resources at the NSF.

Supply Chain Disruptions Task Force. The Biden Administration completed a 100-day review of supply chains, which was one of the requirements in an executive order earlier in the year. Among other items, the resulting report called for the creation of a Supply Chain Disruptions Task Force, which will “provide a whole-of-government response to address near-term supply chain challenges to the economic recovery” across several key sectors, including construction, agriculture, semiconductors and transportation. The task force will be led by the Secretary of Commerce, Secretary of Agriculture and Secretary of Transportation.

Given the expertise on supply chain management among INFORMS members, INFORMS sent letters to President Biden and each of the agency leaders on the task force requesting that they engage INFORMS members on all supply chain issues, including those related to risk management, global sourcing, capacity management and information technology integration. Positive feedback from administration officials has already been received.

INFORMS members have been actively engaged with policymakers and the press on supply chain resiliency and innovative solutions to prevent disruptions. Engaging with the task force is yet another opportunity for experts within our community to help advance solutions for the federal government.

National Artificial Intelligence Research Resource Task Force. The Biden Administration recently announced the National Artificial Intelligence Research Resource Task Force, which was mandated by legislation passed last year. The task force will “write the road map for expanding access to critical resources and educational tools that will spur AI innovation and economic prosperity nationwide” and create a blueprint for the “National AI Research Resource – a shared research infrastructure providing AI researchers and students across all scientific disciplines with access to computational resources, high-quality data, educational tools and user support.”

The NSF and White House Office of Science and Technology Policy (OSTP) will lead this task force. INFORMS has requested that NSF and OSTP engage our members on the work of the task force and the development of the National AI Research Resource.

Press ActivitiesEngaging with the media is an important aspect of our broader advocacy activities and it furthers our efforts to champion the O.R. and analytics profession, as well as position INFORMS members as subject matter experts. Behind the scenes, this takes consistent work to identify novel hooks for reporters, build and nurture relationships, and a variety of other efforts from the advocacy team.

You may have seen some of these stories in INFORMS social media posts or elsewhere, but what is not seen is the full scope of this work. For instance, in the second quarter of this year alone, INFORMS’ public affairs team pitched and placed nearly two dozen INFORMS members who appeared in more than 300 stories in the media. If you haven’t seen these stories, they can all be found in INFORMS News Room at https://www.informs.org/About-INFORMS/News-Room.

The number of INFORMS members involved in our advocacy and communications activities is continuing to grow. Nearly 100 people from across our communities have volunteered to participate, and we look forward to partnering with more INFORMS members in these important key efforts. Please feel free to get in touch with me at [email protected] or INFORMS public relations specialist Ashley Smith at [email protected] to learn how you can be involved in INFORMS’ advocacy and communications activities.

JEFFREY M. COHEN ([email protected]), MBA, serves as INFORMS director of public affairs & marketing. Connect with him on LinkedIn at linkedin.com/in/jmcsc.

REFERENCES1. Jeff Cohen, 2021, “2021 Policy Focus Shift,” OR/MS Today, April 1,

https://pubsonline.informs.org/do/10.1287/orms.2021.02.20/full/.2. Ashley Smith, 2021, “INFORMS Endorses Bipartisan, Bicameral

Legislation to Modernize STEM Education,” July 1, https://www. informs.org/About-INFORMS/News-Room/Press-Releases/ INFORMS-Endorses-Bipartisan-Bicameral-Legislation-to- Modernize-STEM-Education.


FORUM

PREDICTIVE AND PRESCRIPTIVE ANALYTICS’ ROLE IN FIGHTING COLLEGE ATTRITIONBY BRYAN BELL

BETWEEN DWINDLING ENROLLMENT rates and budgets tightening, the past year has increased stress on higher education institutions to not only meet enrollment goals, but hold onto the students they already have and ensure degree completion. In fact, every year, we see the “summer melt” [1] push nearly one-third of students off college campuses – and this year was even worse as students faced new challenges brought on by the pandemic.

The good news is that predictive analytics can help institutions in their fight to retain students, even amidst such tumultuous conditions. We’re already seeing the benefits of data analytics applied to student enrollment and retention, with some institutions reducing their summer melt attrition by approximately 1% in fall 2019 [2]. According to the “2020 Higher Ed Enrollment Trends Pulse Report” [3] by Othot, “institutions that actively used predictive and prescriptive analytics throughout the 2020 enrollment cycle countered the national trend of steep enrollment declines.”

Predictive and Prescriptive Analytics in Higher EducationPredictive analytics focuses on the future while prescriptive analytics determines immediate actions that institutions can take to improve enrollment and retention. Using modern data science methods, institutions can implement both predictive and prescriptive analytics to define how student and

prospect marketing campaigns are performing and make suggestions for changes that will create better student reception and action.

For example, Florida Institute of Technology increased enrollment rates in 2020 by 3% compared to 2019 enrollment by using predictive and prescriptive analytics. Another example is Texas Tech University, which grew its enrollment to 40,322 in 2020, 322 more than its original enrollment goal of 40,000 – with the largest freshman class in the university’s history. On top of that, Texas Tech increased enrollment by a whopping 9% and student retention by 2.6% across a three-year span simply by using predictive and prescriptive analytics.

In Texas Tech’s case, predictive and prescriptive analytics came into play to determine students with a high likelihood of enrolling and persisting and how marketing efforts would influence those outcomes, e.g., which students out of 300,000 would get a mailer. Ultimately, Texas Tech was able to increase the prospect pipeline while working with a flat budget in 2020 by being more strategic through the use of analytics.

Increasing Conversions at Every Stage of the Enrollment ProcessTaylor University, a Christian liberal arts university in Indiana and ranked as the No. 2 college in the Midwest region, is also looking to the power of analytics to meet its enrollment goals. Using advanced analytics and data science methods,

the institution set out to increase conversion rates in every stage of the enrollment process to fight competition around prospective students and enroll individuals who were a better fit and more likely to complete their degrees at Taylor University. This would ultimately allow the university to increase tuition revenue, even without growing headcount because it meant they were enrolling students who would actually stick around.

Using a combination of around 60 data points including metrics such as GPA, academic interests, test scores, engagement and more, Taylor University’s enterprise data systems team harnesses predictive analytics to determine which variables have the highest predictive strength in each step of the enrollment process. As admissions recruiters review an application pool of more than 50,000 potential students, predictive analytics helps them identify students with the best fit for the university and highest likelihood of retention to fill the institution’s 500 open spots. With a combination of predictive analytics and a proactive recruitment strategy, Taylor University has seen record growth.

After only one year of implementing predictive analytics into its enrollment cycle, Taylor University enrolled the largest freshman class in its history. A year after that, the university again recruited a record freshman class, which came in as the fourth-largest class in school history.

The Community College ChallengeWhen it comes to community colleges, these institutions face a completely different challenge. On top of the hard work behind recruiting and enrolling new students, community colleges face a higher rate of attrition than traditional institutions. Why? Many students attending these types of institutions face pressure from outside factors such as full-time jobs, family obligations, transportation challenges and more. The focus here is on retention. Predictive analytics can guide intervention strategies for struggling students at community colleges.

According to Brian Merritt, chief academic officer and vice president for learning and workforce development at Central Carolina Community College (CCCC), the past year has meant many students lost income and are stressed from the myriad pressures facing them, leading to the greatest student drop in nearly a decade for community colleges. However, he says, “the more nimble we can be to make needed adjustments to our policies and practices, the more likely we can keep their momentum going through this pandemic.”

CCCC is using predictive analytics to do just that. Gathering data from data sources such as the student information system, learning management system, enterprise resource planning system and more, predictive analytics allows the institution to make better predictions around students’ potential for degree completion and retention. It also allows the academic advisors and success coaches to have better, more engaging interactions with students and prompts them to intervene when red flags arise.

Community colleges also face the same pressures around recruitment and enrollment as traditional institutions. CCCC is facing these challenges head-on by using predictive analytics to give recruiting, marketing and enrollment staff direction for where to focus efforts based on geodemographic data and other information.

Using predictive analytics, CCCC has increased retention by 9% for full-time students and 18% for part-time students on average since 2012. Increased retention has also yielded higher graduation rates for the institution, with a 19% increase in students completing their degrees – that’s thousands of students.

Predictive analytics and prescriptive analytics have the potential to make a huge difference for higher education institutions struggling to enroll and retain students, and for students struggling to stay engaged. By enlisting the power of technology, higher education institutions can make a difference in millions of students’ lives by helping them fight for their future.

BRYAN BELL is chief data scientist at Aviso Retention, where he is responsible for analyzing data and ensuring that Aviso Retention’s efforts are adequately aligned with the needs of students and education leaders. A solutions-driven data scientist and entrepreneur with a passion for data storytelling, Bell uses data science to support the overall mission of Aviso Retention. He is also responsible for the Aviso Predict product, a collection of risk models created using data science methods to describe risk at institutions of higher education. Prior to Aviso Retention, Bell launched Target Enrollment Group in 2008, where his passion for applying database marketing and predictive analytic techniques to consumer behaviors, coupled with a desire to help institutions provide better recruiting and student support experiences came to fruition. Connect with him on LinkedIn: https://www.linkedin.com/in/avisobryan/.

REFERENCES1. Patrick O’Connor, 2018, “Summer Melt: Why One Third of College-

Bound Students Don’t Make It to Campus in the Fall,” U.S. Department of Education, June 8, https://www.ed.gov/content/summer-melt-why-one-third-college-bound-students-dont-make-it-campus-fall.

2. Georgia Mariani, 2019, “How 4 Universities Use Analytics to Improve Graduation Rates,” eCampusNews, June 4, https://www.ecampusnews.com/2019/06/04/analytics-improve-graduation-rates/.

3. “2020 Higher Ed Enrollment Trends Pulse Report,” Othot.


ISSUES IN EDUCATION

a fixed mindset that they are not capable of learning quantitative material. Thus, the lack of learning is propagated and compounds over a long period of time.

Prior knowledge can have a positive or negative influence on a student’s learning experience [3]. Learning is a function of preexisting neuronal networks, and these networks impact the way information is stored and retrieved. Thus, students with previous neuronal networks with strong connections and accurate information usually progress faster than those with weak or nonexistent neuronal networks. This does not mean that students with weak backgrounds are doomed to fail – they simply may need more deep effort and practice to build the proper foundation to succeed.

From another standpoint, mechanizing concepts in previous classes (e.g., in a high school algebra course) can cause a lack of strong understanding, and thus a weak or nonexistent neuronal network. Therefore, if a student has made a habit of mechanizing how to compute rote problems, they may have a harder time understanding the intuition behind complex scenarios as opposed to a student with little to no previous exposure. Essentially, the student needs the proper prior knowledge to have a good foundation to succeed in the course.

What Instructors Can DoA lack of immediate results may cause some students to be disappointed. A response is for instructors to endorse a growth mindset [4] as opposed to a fixed mindset in the classroom. Making students aware of the difference between growth and fixed mindsets and how they impact learning can help students increase motivation levels. Intelligence can also be learned through practice, and it is not necessarily a static trait [5]. This reinforcement can also help boost motivation levels for students who perceive that they are not smart (or the often used, “I’m just not a math person! ”). Again, the more that students tell themselves that they are incapable of being successful in quantitative courses, the more that they will perpetuate that myth and ultimately prevent themselves from being successful.

In many undergraduate management science courses, students have vastly different levels of prior knowledge, thus making the job of the instructor

challenging on how to pace the material in the class. Students can build these networks through effort and practice, but it may not come easily or in a timely manner. One technique we employ is to issue an algebra review assignment prior to the start of the semester, which has yielded some positive results. In addition, we provide a probability review section prior to introducing Bayes’ theorem and decision trees.

In the algebra review, we ask students to plot and solve systems of linear equations, translate word problems into systems of linear equations, and plot systems of linear inequalities. A student with these skills will have an easier time learning and understanding how to shade a feasible region, identify corner points of a feasible region, and use the level curve approach to graphically solve a linear program. In the probability review, we focus on understanding conditional probabilities and complementary events. It is important for students to understand these concepts to properly construct decision trees and calculate associated probabilities. It also demonstrates to students that the material can be conquered by approaching it in small steps, and this can help reduce their anxiety regarding the course.

We strive to convince students that hard work and practice can increase motivation and build a stronger neuronal network in any subject matter. There is no fixed level of intelligence – students can improve their abilities to succeed in quantitative courses with the right mindset and training.

BABACK VAZIRI ([email protected]), Ph.D., and LUIS NOVOA ([email protected]), Ph.D., are assistant professors in the Department of Computer Information Systems and Business Analytics in the College of Business at James Madison University.

REFERENCES1. Ausubel, D., Novak, J.D., Hanesian, H., 1978, “Educational Psychology:

A Cognitive View, Second Edition,” Holt, Rinehart and Winston: New York. 2. Cochran, J.J., 2012, “You Want Them to Remember? Then Make it

Memorable! Means for Enhancing Operations Research Education,” European Journal of Operational Research, Vol. 219, No. 3, pp. 659-670.

3. Ambrose, S.A., Bridges, M.W., Lovett, M.C., DiPietro, M., Norman, M.K., 2010, “How Learning Works: 7 Research-Based Principles for Smart Teaching,” Jossey-Bass: San Francisco.

4. Dweck, C.S., 2006, “Mindset: The New Psychology of Success,” Ballantine Books: New York.

5. Boaler, J., 2019, “Limitless Mind: Learn, Lead, and Live without Barriers,” HarperOne: San Francisco.

THE ROLE OF PRIOR KNOWLEDGE IN UNDERGRADUATE BUSINESS ANALYTICS COURSESBY BABACK VAZIRI AND LUIS NOVOA

AS INSTRUCTORS, WE STRIVE TO CREATE AN ENVIRONMENT for our students that encourages them to work hard and be motivated to learn. However, this can be challenging in quantitative courses, especially in undergraduate business analytics courses. Though many business programs require some form of a business statistics and management science course, many students are reluctant to put forth effort in these courses if they believe they simply are not “good at math.” In this article, we discuss the role prior knowledge plays in undergraduate management science courses, and some potential ideas to mitigate its negative effects.

The common topics covered in undergraduate management science courses include but are not limited to linear programming, sensitivity analysis, break-even analysis, decision trees, queuing theory, simulation, forecasting and regression. There are many key skills taught at the K-12 level that are necessary to be successful in these topics – namely, algebra and basic probability concepts. A student that lacks these skills may find the subject material difficult and thus conclude that they are not fit for quantitative courses. However, it is important for students to understand that their prior knowledge in these subjects plays an integral role in their ability to succeed.

Necessary Prior KnowledgeWhat a student already knows is one of the most important factors influencing their learning experience [1]. A lack of necessary prior knowledge can cause many undergraduate students to be intimidated by quantitative material. If students are poorly prepared and have little to no exposure to quantitative material, they may even become anxious about prerequisite math skills [2]. Of course, there is a natural negative feedback loop for students in these situations – the earlier they are unprepared, the earlier they start to endorse

THE PODCAST THAT BRINGS OPERATIONS RESEARCH & ANALYTICS TO LIFE!Available at orms-today.org/podcasts, Stitcher, iTunes, Google Play & Spotify


INFORMS PRIZES & AWARDS

ON APRIL 13, WE WERE EXCITED TO LEARN that the Department of Business Analytics in the Tippie College of Business at the University of Iowa [1] was named the INFORMS UPS George D. Smith Prize winner. This was the department’s first time applying for the prize, but it had been a goal for many years.

In 1979, the Department of Business Analytics was established as the Department of Management Sciences. Our niche has been the technical side of operations research (O.R.) and information systems, with faculty having educational backgrounds and research specialties in fields such as data science, social network analysis, optimization, machine learning, statistics, transportation and supply chain management. The department has a thriving Ph.D. program with approximately 20 students doing research with faculty on exciting topics in areas of descriptive, predictive and prescriptive analytics. Thus, our department was in the perfect position to capitalize on the rise in popularity of analytics education. The right ideas at the right time led by innovative faculty with support from college leadership were key.

The Tippie TransformationThe department began its transformation in 2013 by offering an undergraduate degree in business analytics and information systems (BAIS). The curriculum was developed over several meetings among faculty at the Iowa City pub Joe’s Place! In this program, students take a core set of classes covering topics such as business analytics fundamentals, Python programming, database

management and data mining. Students can then choose electives to focus their STEM degree toward particular job opportunities. For example, some students are interested in learning about optimization techniques and how to manage big data, while others may focus on cybersecurity or business process automation. Over time, communication skills have been carefully woven into the curriculum – a departure from similar programs – which employers notice. All students complete their degree with a real-world capstone project with an external client.

With high-quality instruction and high demand for students with these skills, enrollment has grown dramatically. As of spring 2021, BAIS was not only the second largest major within Tippie, but the second largest major at the University of Iowa, with more than 400 students (38% female!). The current challenge is to maintain quality at this new, larger scale.

Soon after the start of the undergraduate major, the department launched educational opportunities for its professional students. Tippie has a high- ranking MBA program that offers evening classes for professional students at three different locations in Iowa. Many working professionals, such as those in insurance and banking, sought to add analytics skills to their resumes, so the department entered the field via a graduate certificate in analytics and built up to a full Master of Science in Business Analytics (MSBA) in all three MBA-serving locations. The first five courses are designed as solid fundamentals in subjects from data science to data programming and advanced analytics and comprise the certificate. In the remaining courses, students dive deeper into specific topics including text analytics, healthcare analytics, data visualization and an experiential capstone. Students can complete the certificate and degree at their own pace.

In 2017, Tippie added a full-time, on-campus MSBA program. The curriculum is similar to that of the part-time program, but at an accelerated pace. Students begin the three-semester program in the fall, complete an internship or research experience in the summer and graduate the following fall. By the end of this program, students can clearly show that they understand business problems, solve problems with data and communicate the results. The full-time degree was launched at the same time as the master’s in finance, leading to a popular joint program where students can receive both degrees in two years. With about 110 full-time students, the department has more than a thousand students across all of these new programs.

It Takes an Analytics VillageA big part of the successful evolution in analytics education at the Tippie College of Business is the Tippie Analytics Cooperative (TAC), which coordinates educational and research connections with external partners, and the affiliated TAC Advisory Council, which consists of leaders in business analytics and information systems, many

of whom are Iowa alumni. External partners include professionals who hold positions including chief data scientist or lead data analyst and come from companies including Clarivate, Principal Financial Group, HMS, John Deere and ACT. At each council meeting, we spotlight one of the department’s programs and the curriculum is reviewed, including discussions of programming languages, new electives and other changes. The resulting feedback directly shapes the department offerings, so graduates at all levels are prepared to meet the evolving demands of the industry.

The TAC Advisory Council has also influenced the topics covered in the department’s major-specific professional prep courses. At the undergraduate level, this course offers students a deep dive into typical careers and exposure to industry professionals. Class sessions focus on topics such as preparing for interviews and how to build a professional portfolio. At the master’s level, this course is two semesters, giving students exposure to a variety of information that does not clearly fit into academic coursework, e.g., new analytics tools, project management principles and dealing with ambiguity in a data case.

The Department of Business Analytics also hosts annual special events for its students. In a one-day Women in Analytics Leadership Conference, students hear from female leaders from both the academic and industrial sides of business analytics and are given the opportunity to network with women working in the real world of data analytics to learn more about how to succeed in the industry. The department also hosts the Iowa Graduate Business Analytics Case Competition, a first-of-its-kind competition that gives graduate analytics students from across the globe a chance to gather and solve a real-world business problem. The most recent competition in April 2021 included a case on vaccine distribution from the Bill and Melinda Gates Foundation.

Over the last 10 years, there have been many exciting developments in the department – including (and perhaps most notably) the renaming of the department to the Department of Business Analytics in 2019 and earning the INFORMS UPS George D. Smith Prize in 2021. That said, the culture of continuous improvement at Tippie means the future is even brighter. Next steps include a fully- online business analytics graduate certificate, curricular additions including techniques for correcting algorithmic bias, and a workshop for future faculty in business analytics (FutureBAProf) to promote diversity in this exciting field.

ANN MELISSA CAMPBELL is the Clement T. and Sylvia H. Hanson Family Chair and department executive officer in the Department of Business Analytics at the University of Iowa.

W. NICK STREET is associate dean for research and Ph.D. programs at Tippie College of Business and Tippie Research Professor in the Department of Business Analytics, University of Iowa.

REFERENCE1. https://tippie.uiowa.edu/business-analytics

THE EVOLUTION OF ANALYTICS EDUCATION AT TIPPIE2021 UPS Smith Prize winner showcases its road to victory.BY ANN MELISSA CAMPBELL AND W. NICK STREET

Source: Tippie College of Business


INTERNATIONAL O.R.

IN MANY ITALIAN HIGH SCHOOLS, IT has become common practice to schedule oral examinations throughout the school year, according to a predefined calendar, for students to maximize their performance by knowing the exact date of their exam. Many years ago, this practice was considered uneducative and all students were required to be ready for examination every day. But times change, and a father who grew up in these “good old times” had the chance to suggest to his daughter that this examination plan could be optimized – if you can’t avoid it, optimize it!

When Chiara was attending high school, her teachers did not take responsibility for planning the oral examinations, but they did require a certain number of students to be ready for exams on predetermined dates. However, when the class was faced with the task of assigning students to exam days, the students experienced lack of transparency and trust if it put a single student in charge of planning, and lack of time to collectively define the plan when the students tried to plan together. Frequent complaints and a widespread feeling of unfairness were an obvious result. If you experience this as a student and you have an operations research (O.R.) professor at home, well, it’s time for you to take the initiative and insert a bit of optimization into your school. Chiara and her father spent a couple of hours with paper and pencil, and out came a mathematical optimization model.

The ModelAssigning n students to n time slots is nothing more than a bipartite assignment problem. However, the main issue was to optimize fairness above all, measuring it according to student preferences. Since

each examination round was typically organized for five days with about five students per day, it was decided to represent preferences on a 1-to-5 scale, with 1 meaning “best choice.”

Now, what should be the objective function? Optimize (i.e., minimize) the overall preference score? Or the worst preference score? Giovanni took this opportunity to show his daughter that there is a trade-off between efficiency and equity; this is a general rule, not depending on this specific case study. The next observation was that the problem of optimizing the worst preference score (a “min-max” objective function) typically yields several optimal equivalent solutions; this is less likely to happen when optimizing the total score (a “min-sum” objective function). Therefore, it was a natural choice to optimize equity first and efficiency second, in a hierarchical fashion. During the second optimization run a suitable constraint imposes that no assignment is allowed to worsen the optimal value of the first objective.

The Many Faces of Planning The free solver glpsol was the chosen tool for the problem. Preferences were collected with a Google spreadsheet before every examination round. The O.R.-based planner was introduced at the beginning of the 2019-2020 school year, when Chiara began her fourth and penultimate year, and it was immediately well-accepted. It was clear that an automated planning tool was the solution to avoid endless discussions and arguments, but little by little additional features became evident.

As expected, the initial experience soon suggested there was room for improvements. Sometimes teachers sought additional flexibility, allowing for a variable (but lower and upper bounded) number

of students per day. Some equality constraints were promptly replaced by inequalities. Every now and then, teachers allowed the students to select exams days within a set of possible days. This was a good opportunity for Chiara to learn the usefulness of binary variables (bipartite assignment is not ideal for this purpose, because integrality constraints are not actually required to obtain binary optimal solutions). Some teachers dared to impose more sophisticated constraints, such as, “When I examine student x, I do not want to examine more than k students in the same day.” However, all these requirements could be easily translated into linear equations and inequalities, enriching the integer linear programming model.

An important feature added to the model was the ability to manage the exam history, so that fairness was independently pursued not only for each examination round, but also for the entire school year. For this purpose, a “credit/debit” system was defined to represent the quality of assignments received by each student compared with the class average. This was represented by a preference correction factor with no need to change the rest of the model.

The O.R.-based planning tool proved invaluable in several circumstances when unpredictable events forced teachers and students to change their plans, allowing for fast rescheduling. At the end of the school year, after 16 examination rounds, the average score of the students-to-day assignments on the 1-to-5 scale was an astonishing 1.41, with individual scores ranging from 1.19 to 1.56.

A Happy Ending At the end of her fifth year, Chiara approached the final examination to receive her scientific high school diploma. For this last step, students were required to discuss a personal project related to mathematics and physics. For Chiara, the choice of the subject was pretty obvious: O.R.!

In preparation for the final exam, she first presented a one-hour seminar to her class (and her math teacher) describing the mathematical model she had been using for two years. (Figure 1 shows a slide from Chiara’s final presentation.) The math teacher was amazed; she could not imagine that her students could use math to solve problems of their ordinary school life. Chiara’s schoolmates were fascinated; for two years they had no desire to learn how “Chiara’s algorithm” worked, because they believed it was too difficult to understand. On the contrary, they discovered that once the problem was correctly modeled in mathematical terms, the job was done. Some of her classmates, evidently talented in economics and business, were so engaged that they proposed developing an app version and selling it to the other classes. Chiara’s presentation ended with a demo, so that everybody could appreciate the rapidity of the planning process.

Chiara’s project for the final diploma exam was entitled “Discrete and continuous in math and physics.” The math portion concerned continuous and discrete optimization. Owing to anti-COVID restrictions, Chiara’s proud father was the only accompanying person allowed in the audience, where he had the pleasure of listening to his daughter illustrate the birth of O.R., its purpose and its connections with the history of the 20th century, as well as concepts like combinatorial explosion, branch-and-bound and the use of linear programming relaxations to solve integer programming problems. An unforgettable day for both!

A Young O.R. AmbassadorOwing to this unusual experience, Chiara has probably become the youngest (international) O.R. ambassador ever. However, this is not an isolated case; several examples in different countries have shown that the basics of mathematical optimization are perfectly suited as educational topics for high schools. There is no reason to classify them as “academic stuff ” or “university-level math.” Today, mathematical optimization is an essential component of STEM education, and there are plenty of opportunities to allow students to appreciate its power in solving real problems.

CHIARA RIGHINI received her scientific high school diploma from Liceo Scientifico “Leonardo da Vinci” in Crema, Italy, with full marks and cum laude in June 2021.

GIOVANNI RIGHINI is an O.R. professor at the Department of Computer Science, University of Milan, Italy.

OPTIMIZATION IN HIGH SCHOOLS: ORAL EXAMINATIONS PLANNINGBY CHIARA RIGHINI AND GIOVANNI RIGHINI x1

z

1

z* (P) z* (R)x2

FIGURE 1: A slide from Chiara’s final class presentation shows bounding from linear relaxation.

Today, mathematical optimization is an essential component of STEM education, and there are plenty of opportunities to allow students to appreciate its power in solving real problems.

22 ORMSTODAY.INFORMS.ORG I AUGUST 2021 I INNOVATIVE EDUCATION ISSUE 23INNOVATIVE EDUCATION ISSUE I ORMSTODAY.INFORMS.ORG I AUGUST 2021

Innovative Education Issue

Flipping the Classroom

Undergraduate Supply Chain Curriculum

Op-ed: Anti-racist Teaching Practices

Better Practices for Teaching Business Analytics

Quantum Computing in OR/MS Education

History of O.R. at Stanford University

REMOTE LEARNING PROMPTED BY COVID-19 HAS challenged educators at all levels, but it has also provided an enormous opportunity for innovative course content delivery and new access to educational technology tools. The authors leveraged this opportunity and access by “flipping the classroom” for an undergraduate engineering probability class at Northwestern University, replacing in-class lectures with hands-on problem solving, with the goal of providing an experience that helped students learn while facing the challenges of COVID-19; bringing real-world context into the classroom; actively engaging students regardless of whether their classes were taken through a computer screen or face-to-face; and fostering classroom community even for physically distant students. How could we create an experience that accomplished all of these objectives? And in the end, what did students think of this experience? All of these points will be discussed, but let’s start by sketching what our classroom model looked like.

ELOISE CHUDIK,

JILL HARDIN WILSON,

DAVID MORTON and

EOJIN HANfliPpedthe

classroomRemote learning best practices for the best probable outcome.

by


How it WorksIn the flipped probability course, all lectures are prerecorded and delivered asynchronously, allowing for synchronous class time to be used more intentionally. The flipped classroom model aims to meet the following objectives: actively engage students, meet learning objectives, avoid screen burnout and foster community. To meet these objectives, in any given week, students are expected to watch and reflect on all lecture content, prepare a draft of the week’s case study prior to the discussion session, attend synchronous discussions, and individually complete a problem set. A more detailed description of the class structure, its development and its implementation can be found in an article, “Flipping Out for Probability” [1].

This article focuses on student reactions to some of the key features of the flipped classroom model.

How Students Responded Asynchronous lectures. Asynchronous lectures are prerecorded and split up into small, digestible pieces, typically around 10 minutes in length. These videos made students responsible for consuming lectures on their own time – a big change from the traditional classroom structure. However, students reacted to asynchronous lectures in an overwhelmingly positive way.

Second-year industrial engineering (IE) student Emily Hull took the class from her apartment in Evanston, Ill., in the fall of 2020. She described how the structure of asynchronous lectures allowed her to stay focused and avoid screen fatigue. “In the era of online school and Zoom burnout, it was nice to have the videos split into smaller, digestible parts,” she says. “It is much easier to watch four videos, each 20 minutes or less, than it is to pay attention to an hour and a half lecture.”

Denise Sullivan, another second-year IE student, took the same probability course in fall 2020. She also appreciated the asynchronous lectures, but for a different reason. “The asynchronous lecture made it easy to get ahead and to look back on things that were confusing,” she says.

Like Sullivan, many other students felt that being able to watch lectures on their own time provided them the opportunity to more easily revisit confusing topics, as well as a more flexible work schedule. However, students still acknowledged that the live discussion sessions were crucial in keeping them on track. Our next key feature addresses why.

Synchronous sessions. Synchronous discussions incorporate time in breakout rooms for students to collaborate with a group on responses to a case study. These sessions also dedicate time to clarify lecture material and engage in meaningful Q&A with the professor. Hull said that she personally felt very engaged in discussion sections and regarded them with the same importance as the content videos. Sullivan agreed on the prior, explaining that previously watching all of the lectures and being

able to ask questions during the entirety of the discussion section allowed students to get all of their questions answered. Another classmate from the fall 2020 semester reflected on the discussion sections. David Meza, a second-year IE and economics student, took the probability course from Miami. Reflecting on the discussion sections, Meza says, “I never felt like I was wasting time in class, and I was always engaged.”

Apart from actively engaging students, these synchronous discussion sessions were used to help foster a sense of community within the probability classroom – something that was especially important last fall, when most first- and second-year students were not allowed on Northwestern’s campus. Annie Tsui, a second-year IE student who took the course from her bedroom in California, says that group collaboration “allowed me to make real friends in the class that I now study with and build my IE schedule around … so I can take [more] classes with them.” Several other students indicated that they built real connections throughout the course. Charlotte Oxnam, a second-year IE student who took the probability course from Evanston, Ill., even went as far as to say, “I have made more genuine connections in IEMS 202 than in the last three quarters of my remote classes combined.”

Third-year student Marina Siqueira, studying economics and IE, also found that the breakout rooms and group collaboration were extremely useful in building connections with fellow students. However, her experience was a little different. Taking the class from her home in Brazil, Siqueira was not only physically distant, but she was new to the IE program. “Coming in from a different major and having to take this class a little later in [my] schedule meant that I didn’t know anyone in the class,” she says. “It was great to have a safe space to get to know people, exchange contact information and get to work together!”

Case studies. The synchronous sessions have a significant impact on the way students collaborate in the probability course. A large portion of these sessions are spent on another key feature of our flipped probability classroom: case studies. Case studies consist of short narratives that bring context and purpose to the models and methods of probability, and include topics such as COVID-19 testing, renewable energy generation, portfolio management, and the popular game show, “The Price is Right.”

Many students felt that the case studies provided context and helped to ground abstract concepts. Oxnam explains that she was able to learn better through applying lecture content to the real-world examples in a case study. She referred to a case study about the accuracy of COVID-19 testing and adds, “I don’t do well when I am told just to memorize an equation, but if I can see how the equation fits into an example, I will remember it much longer. All of [a] sudden it wasn’t just remembering what was X

and what was Y, it was a positive test or a negative test.” Fellow classmate Natan Gamliel, a second-year computer science student who took the probability course from Evanston in winter 2021, also found the COVID-19 case study particularly interesting, “in part because of its relevance to the times but in part because it explained details about the statistics relevant to the tests that I would not have understood otherwise,” he says.

One student found the case studies particularly helpful in understanding probability, but not because of its real-world relevance. Ploenta Voraprukpisut, a third-year IE major, who took the course from Bangkok, Thailand, says, “The case draft assignments gave me an opportunity to ‘test’ my understanding of the topic in a low-stress manner. By grading on completion, rather than correctness, this motivated me to figure out how to solve the cases myself before seeking help elsewhere.”

What Students LearnedAlthough the students reacted positively to the key features of the flipped probability classroom, we still needed to ask: Did students learn what they needed to learn? Along with student engagement and collaboration, we need to create a classroom model that is effective in helping students learn probability and prepare them for the next courses in the IE sequence. Having observed no significant change in exam grades and homework scores from a previous traditional offering, we turned to survey data and student testimonials to answer this question.

We compared course survey results from the flipped model in fall 2020 and winter 2021 with those from fall 2019, when the course was offered in person (all sections were taught by the same instructor). Student ratings for both fall 2020 and winter 2021 revealed similar or slightly increased scores for both overall course and instructor ratings. However, most striking was the dramatic increase in the students’ view of the usefulness of the discussion section, suggesting that the synchronous discussions, involving case study discussion, Q&A with the professor and group collaboration, were effective in helping students learn.

These ratings were brought to life when students were asked to reflect on how much they learned in the course and their preparedness for the following courses in the IE sequence. Many students indicated that moving onto the next course in the sequence was not difficult because of their strong foundation in probability; Gamliel is one such student. He credited his confidence to “the course structure requiring that we practice the new material almost immediately after learning it.” Tsui shared similar confidence in the material and has already applied her probability skills to the next courses in the IE sequence. “I feel confident in the material I learned, I have already successfully used the content learned in my continuation of IE courses and it was very helpful,” she says. Other students, including Siqueira,

are taking classes even further on in the IE sequence and suggest that they are performing well because of their strong understanding of underlying probability concepts.

To further understand students’ level of preparedness, the authors spoke with three professors who teach three undergraduate courses that directly rely on probability. The professors indicated that they cannot provide hard data or concrete comparisons between students who took the probability course with the traditional format and those who took it with the flipped format, because of the significant changes that COVID-19 has presented. That said, the professors unanimously agreed that students appear to be at least as well-prepared relative to students in previous years. Professor Chang-Han Rhee, who teaches a stochastic modeling course, provided some more insight into the performance of his students from our flipped classroom format. While stopping short of causal inference, he pointed out that:1. Students seem generally more curious

about the “why” than in previous years.2. Several of the students with the highest

level of participation came from the flipped probability classroom.

3. Students from the flipped probability classroom scored approximately 5% higher on the midterm exam than students who took the traditional probability course.

4. The primary difference in the midterm exam scores came from the two questions that involved topics from the introductory probability course.

Professor Rhee’s observations suggest that at least some of the objectives for the flipped classroom have been met. Students seem to have learned the material well and are deeply engaged in their learning.

The Flipped Classroom Post-pandemic While COVID-19 wasn’t the primary motivation for flipping the classroom, it certainly played a role in developing our new classroom model. It is very evident that parts of the flipped classroom, such as asynchronous lectures, work well during the pandemic when students are in different time zones and environments, and facing other challenges. However, we envision employing this model after the era of COVID-19. The next big question: How do we keep the best parts of the flipped classroom model while incorporating everything students and educators love about in-person learning?

It is evident that nothing can replace in-person learning. All students agreed that given the choice they would be learning on campus, in the classroom, face-to-face with fellow students and professors. Yet, COVID-19 revealed some ways that learning can be enhanced even when classes are “back to normal.”

classroomthe

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Hull provided some interesting insights on office hours. “Last year [2019], I rarely attended office hours because I never thought my questions were worth walking all the way to an office to potentially wait in line,” she says. “Now, with office hours only a click away, I find myself going almost weekly in each class. I feel much more comfortable logging on to ask a quick question and then maybe even coming back once I make progress and have a new question.”

Professors should consider offering mixed methods of office hours, possibly one day of remote office hours and one day in person. It is important for students to feel comfortable interacting and building a relationship with their professors face-to-face, but remote office hours also provide a more convenient means of asking quick questions.

Overall, the biggest advantage of remote learning, for most students, is having access to recorded lectures, and so we encourage professors to continue to record and post lectures once in-person class has made a full comeback. Gamliel says, “Beyond providing access to the coursework for people in distant time zones, recordings have been a convenient way to easily review a piece of material that may be giving me trouble.” Not only are recorded lectures helpful for rewatching content, but they also make learning more accessible to students with disabilities or chronic illnesses [2].

Incorporating case studies and discussion into in-person learning can also increase student engagement. Case studies use real-world context that helps make the material “stick.” The case studies are made more meaningful when supported with a designated discussion space to dive more deeply into complex topics, ask meaningful questions and collaborate with other students. Siqueira says that she would like to see less class time focused on delivering lectures and more time spent diving

classroomthe

flipPed

deeper into points of confusion and applying those concepts individually and in groups, suggesting that “this is efficient and is a good mix of self- paced learning and interacting with others to solve problems, which are key skills for 21st century jobs.” One way to accomplish this is by keeping one day of in-person class designated to case studies, Q&A and group work, though care should be taken to ensure workload for students does not increase.

Educators still have opportunities to continue to innovate the way in which we deliver course content. We have known what works in traditional, in-person learning, and now we have found new ways to teach remotely. Our next challenge is integrating the two modes to develop a learning experience that captures the best of each.

ELOISE CHUDIK is entering her fourth year as an industrial engineering major at Northwestern University. She has considerable experience supporting educational efforts and is currently completing an internship at Goldman Sachs.

JILL HARDIN WILSON is the assistant department chair and director of Undergraduate Studies in Industrial Engineering and Management Sciences at Northwestern University. She has extensive experience teaching undergraduate probability and optimization and is a recipient of the Charles Deering McCormick Distinguished Professor of Instruction award.

DAVID MORTON is the David A. and Karen Richards Sachs Professor and department chair in Industrial Engineering and Management Sciences at Northwestern University. He is an INFORMS Fellow, and his teaching interests include optimization and probability.

EOJIN HAN is an assistant professor in the Department of Operations Research and Engineering Management at Southern Methodist University. He participated in this project when he was a doctoral student at Northwestern University. His teaching interests include probability, simulation and optimization.

REFERENCES1. J. Hardin Wilson, D.P. Morton, E. Han and E. Chudik, 2021, “Flipping

Out for Probability,” Proceedings of the IISE Annual Proceedings.2. Christine Scherer, 2021, “Post-Pandemic Accessibility,” Northwestern

School of Professional Studies, April 19, https://dl.sps.northwestern.edu/blog/2021/04/post-pandemic-accessibility/.

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What’s Your StORy?Ann CampbellDepartment Executive Officer & Clement T. and Sylvia H. Hanson Family Chair, Department of Business Analytics, University of IowaINFORMS member since 1997

Would you say the work you do saves lives, saves money, or solves problems? I would love to say save lives, but like most academics, I would have to say solves problems. Even though I do a lot of work in disaster relief and last-mile delivery, I’m trying to build insight into what are the best solutions for those problems, such as how to be best prepared for a disaster in terms of what supplies to store and how much. I’m really turning these problems into math and trying to solve the math and then hoping the companies can apply the solution to do those things like save money and stay alive.

You are a member of several INFORMS societies and groups, including the Diversity Community, WORMS, Transportation Science & Logistics and INFORMS Speakers Program, to name a few. Why do you find this involvement so important?My biggest involvement over my time at INFORMS has definitely been with Transportation Science & Logistics (TSL). I’ve been part of the TSL Society the entire time and I’ve been an officer within that group. I think it’s so important – and this is another good piece of advice for people who are earlier in the career – is to find a community where you can have a smaller place within the bigness of INFORMS. For example, I think the INFORMS Annual Meeting can be intimidatingly large and I can see how someone can feel very lost, but when you go to the TSL meeting, or whatever your society is, you’re guaranteed to go to a room with some familiar faces.

Can you talk a little bit about how the COVID-19 pandemic has affected your last year?In terms of how it affected me professionally is – totally unrelated to COVID – I was scheduled to become the interim department head on March 1, 2020 and two weeks into my new job, the world changed, so it affected my perspective on this new position because I didn’t know what normally being department head was like. I’m looking forward to finding that out.

Read Ann’s article about Tippie’s journey to the UPS Smith Prize in this issue (page 18).

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SUPPLY CHAIN MANAGEMENT (SCM) has never been more essential or garnered more attention as the world grapples with the COVID-19 pandemic. The global aspect of supply chains, along with industries’ incompetence and lack of expertise dealing with supply chain risks created by a pandemic, hampered the ability of many businesses to manage their supply chains. The increased frequency and severity of supply chain risks and disruptions in many industries has underscored the necessity to control such risks and create resilient supply chains.

Despite this, introductory SCM course curricula typically lack supply chain risk management (SCRM) topics. Introductory SCM courses generally aim to equip students with fundamental knowledge of supply chain management, and standard SCM textbooks do not adequately cover SCRM approaches and contents [1].

Developing a Full CurriculumWhen preparing to teach an introductory supply chain management course at the undergraduate level for the fall 2020 semester, I began developing the curricula in such a way that students would be exposed to real-world supply chain issues while also being effectively equipped with the updated and practical SCM knowledge commensurate with an introductory SCM course. To bridge the pedagogical gap and effectively bring risk management principles into the course, I chose to enrich the course materials with a university-industry collaboration. To accomplish this, SCRM-related materials

and class activities were adopted from Kinaxis, a leader in supply chain management well-known for delivering supply chain agility and resiliency for many companies worldwide. Kinaxis recently created an academic program [2], through which they will contribute to academia by offering guest lectures, case studies, conferences and events, and more. The program aims to give institutions contemporary, practice-based examples to help students prepare for the workforce.

Here, using my course as an example, I will discuss a pedagogical framework that may be used in conjunction with SCM courses to properly acquaint students with SCRM concepts. Each component of the framework is described, along with ways for implementing the approaches and their intended outcomes.

Discussions on supply chain risks. In each session, students are first taught an SCM concept and demonstrations of its importance and applications; students are then familiarized with related supply chain risks. The discussion themes were adopted from the newsletter “OMintheNews” [3], which includes up to one- minute videos and a wide range of discussions on the most recent news in operations management and SCM. Every session included foundations of each SCM topic, followed by watching a video related to its SCRM aspect and a class discussion, which neatly fulfilled the desire for improved student engagement in class and active learning. Another approach to use the material is to host the discussions in a discussion board within the learning management system (LMS).

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SAJAD EBRAHIMI

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Undergraduate Curriculum Supply Chain Resilience in

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Undergraduate Supply Chain Curriculum

Notably, students emphasized the value of the materials offered by the industry partner; these resources encouraged students to become more interested and focused on the new themes in SCRM.

problem-solving skills to propose solutions. This exercise was the semester’s final course activity, with the expectation that students would have enough exposure to SCRM approaches that would enable them to critically discuss supply chain problems and offer viable ways to prevent/mitigate risks.

As confirmed by the students in their course evaluations and an informal survey, the framework outlined above allowed students to properly understand SCRM concepts and practice their applications. Notably, students emphasized the value of the materials offered by the industry partner; these resources encouraged students to become more interested and focused on the new themes in SCRM. Many students found that communicating with actual decision-makers through the virtual event and guest lectures gave them a stronger sense of authenticity.

ConclusionSupply chain risk management is increasingly becoming a required topic in SCM courses. Instructors must devise efficient techniques for incorporating SCRM principles into their curriculum and practically teaching students the concepts and their applications. I believe that providing most of the materials from one company, specialized in delivering supply chain agility and resiliency to supply chains across the globe, could help students learn and practice the SCRM strategies in a consistent and comprehensive learning environment, and better relate their knowledge and understanding of the concepts to real-world examples. Incorporating even more innovative teaching approaches such as gamification can increase the range of concepts conveyed to students and improve knowledge retention. I believe including a supply chain game in which students practice the most widely utilized SCRM strategies by the industry partner, can significantly add value to this proposed teaching framework and improve students’ awareness of SCRM. (Kinaxis plans to release such a game in the near future.) I hope that instructors find this curricular framework useful in incorporating SCRM concepts and making SCM classes more interactive and exciting for students.

SAJAD EBRAHIMI is an instructor and graduate research assistant in the College of Business, Transportation, Logistics, and Finance Department at North Dakota State University. He is also a doctoral researcher at Upper Great Plains Transportation Institute in Fargo, N.D.

REFERENCES1. Ferguson, M.E., & Drake, M.J., 2021, “Teaching supply chain risk

management in the COVID-19 Age: A review and classroom exercise,” Decision Sciences Journal of Innovative Education, Vol. 19, No. 1, pp. 5-14.

2. Kinaxis Academic Program, 2021, https://www.kinaxis.com/en/about-us/kinaxis-academic-program, April 8.

3. OM in the News – We blog articles featuring Operations Management in the news. Use current events in class. Search by chapters in McGraw-Hill titles. Retrieved June 24, 2021, from http://ominthenews.com/.

Kinaxis virtual joint events. To further familiarize students with the present state of supply chains and application of SCRM strategies, students were given the opportunity to attend a virtual event hosted by Kinaxis. The event included customer testimonials about their experiences managing supply chains during the COVID-19 pandemic. Students also learned about the role of SCRM in improving companies’ operations and profitability. Students benefited greatly from the virtual event because it provided an understanding of the most recent and impactful approaches companies used to build resiliency and agility into their supply chain planning by leveraging modern approaches and concepts such as artificial intelligence (AI), human intelligence, concurrent planning and supply chain digitization. Extra credit was awarded to students who wrote a concise synopsis of an event session.

Guest lecture. A themed guest lecture on supply chain resilience was synchronously delivered to the class, in which an industry thought leader from Kinaxis shared his thoughts on supply chain resilience strategies and experiences dealing with supply chain risks. The experience was highly beneficial for students in that they could tie previous class discussions to the issues covered in the guest lecture and learn more about the concepts by hearing different points of view. It is also worth noting that the guest lecturer highlighted supply chain career paths, which may encourage more students to pursue them in the future.

Case study. Kinaxis also provided a case study that looked into a supply chain disruption issue. Incorporating the case study as one of the class activities allowed students to apply the stated SCRM strategies to a realistic situation, and critically examine and analyze possible solutions. The case study highlighted the importance of visibility and concurrent planning in managing supply chains and increasing their resilience in the event of disruptions. The case study’s close relevance to what most global supply chains and end-users went through during the pandemic, as well as its alignment with the presentations delivered in Kinaxis’ virtual event, attracted students’ attention and helped them more effectively participate in class discussions.

Final project. To encourage students to critically think about supply chain challenges, they were asked to prepare a brief essay on a supply chain issue of their choice. They were invited to analyze a recent supply chain issue reported in the news or scholarly journal. Students were expected to describe the problem and the industry/company affected by it and then outline how the organization could mitigate supply chain risks using SCRM methodologies. The project was aimed to entice students to connect their learning experiences to real-world supply chain risks and apply their

Meet with job seekers and collect resumes from eager candidates

and schedule private on-site interviews. After last year’s incredibly

successful virtual sessions, we are happy to provide another

opportunity to match qualified candidates to top job positions in a

flexible format. Both the in-person and virtual options provide you

with the opportunity to connect with qualified industry and academic

candidates in analytics, data science, AI, machine learning, and more.

• Provide your recruitment materials to a competitive talent pool

• Promote your organization and meet qualified, diverse candidates

• Connect and chat securely with candidates at your virtual booth

• Schedule on-site interviews in a casual setting or private booth

• Connect with job seekers before the meeting

www.informs.org/careerfair In-person Event I Oct. 24–26 Virtual Event I Oct. 20

32 ORMSTODAY.INFORMS.ORG I AUGUST 2021 I INNOVATIVE EDUCATION ISSUE

SOMMER GENTRY

OP-ED

RACIST

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Teaching PracticesANTI-

OUR WORK AS TEACHERS AND INSTRUCTORS CAN CONTRIBUTE TO dismantling racism and building a just society – but how? Many of us have wondered how we can teach operations research (O.R.) in ways that make our classrooms locations of empowerment and progress for underrepresented minority students. This article describes practices I have adopted as a faculty member at an undergraduate institution teaching courses within the O.R. major in pursuit of this goal. Of course, anti-racism is not merely a list of actions to perform, but a personal journey that must include reading and viewing media created by people of color, naming and owning one’s emotions and reactions to racial injustice, and learning U.S. history to understand how racism continues to affect people of color.

Empowering StudentsTeachers should value their students as humans with their own aspirations, motivations and interests beyond the subjects we teach, and be curiously attentive to differences in their backgrounds. This can happen naturally, by engineering ways to hear our students’ voices frequently and respectfully throughout a course. One approach is assigning self-directed projects to allow students to choose applications and illustrations that reveal their passions. I incorporate a show-and-tell assignment into courses such as linear programming or stochastic modeling in which students give 7-minute presentations introducing examples from a mass media article or from their own lives. One student brought a continuous time Markov chain example representing his task focus while studying, switching to playing video games or calling home, calculating the fraction of his evening spent on homework.

One student discussed equitable schedules for the hospital at which their parent worked. Another student, who played on the university’s football team, discussed optimal selection of the next play in a football game. One student brought an example that violates the Markov assumption, and the class discussed the consequences – a textbook with perfectly designed exercises might never prompt that inquiry. When another student shared a facility location problem for disaster recovery sites, the class discussed equitable access for households that lack cars. By inviting students as co-creators of class content, the coursework becomes more transparently relevant to their goals and to real-world impact – this is linked to persistence in STEM for minorities and women [1].

Building Relationships and Offering SupportAsk yourself how much time you spend listening to your students about how they made their way to your course, and what their dreams, hopes and worries are. Ensuring students feel valued, safe and heard makes them comfortable to seek help with the material and motivates them to do their best. Minoritized students in particular need a sense of belonging – of their concerns being cared for – in our field.

Resist the temptation to give up on or blame students who arrive with skill deficits. It is a challenge to teach, say, linear programming to students who struggle with algebra, close reading or graphing equations. It is still our obligation to serve these students, and we might discover other particular strengths they have. We should intervene early and often, in ways individualized to each student. We can direct students to peer tutoring on campus, to our own office hours or to other resources.

Most of the courses that INFORMS members teach are probably not core courses; imagine how dedicated and interested a student must be to

declare a STEM major such as operations research or industrial engineering without a history of high achievement in quantitative courses. As instructors, we must recognize the disparities in primary and secondary education quality in the United States, driven by inequitable funding of education through local tax revenues. Some schools do not have enough certified math and STEM teachers or funding for textbooks, while other schools offer Advanced Placement courses and college credit. Politically, we should work to change the structural racism that disproportionately denies high-quality preparation to minority students. Of course, minorities are not a monolith! We cannot presume to know what any student’s experience has been unless we directly hear it from them. Our role is always to develop someone’s capabilities rather than judge their future potential.

Eliminate Competitive GradingI have adopted standards-based grading, sometimes called mastery grading. A full exposition of how this works is a topic for another article, but the idea is to grade against a fixed standard, using a predetermined list of the desired competencies and skills that constitute the course goals, which eliminates competitive grading. Grading on a curve sets up a competition among STEM students and contributes to disparate dropout rates of women and underrepresented or underprepared groups. Instead, we can create supportive learning environments in which students share their insights and understandings with each other, often through group problem solving during class time. Research shows that less successful college students are those who try to study in isolation, whereas more successful students are those who work substantively with others on problems [2].

Most implementations of standards-based grading allow students to repeatedly try to demonstrate success of a particular skill so that students seeking to improve their grade will spend effort learning what they have missed, rather than asking for extra credit assignments. I also give frequent low-stakes quizzes and assessments, often every single class day. My students embrace these opportunities to receive timely feedback about their progress.

Explicit GuidanceIt is well documented that implicit rules, with which only some communities are familiar, form invisible barriers to educational achievement. Counteract this by making everything explicit. For instance, create and distribute a grading rubric when you give an assignment, and discuss the rubric so students can explicitly judge their work by your standards. Give students best practices for studying, interacting with the textbook, how much time they should expect to spend on homework, and what they should do if they feel stuck on a problem.

Explicitly model active recall as a study method, i.e., prompt students to take one minute to write down what was covered in the previous class session before you start the next lesson, or have students write their own possible test questions with a closed book when reviewing for a test. Explicitly model metacognition (thinking about one’s thinking) and monitoring one’s own solution process. For instance, use a two-column format: starting with a page folded lengthwise, students write the problem computation on the right side and general instructions on the left side, using words to name or justify each step of the computation. Students might not ask for help even when they need it, so reach out to individual students whom you see struggling, invite them to your office hours and establish a path to improvement.

Active LearningStudents should be actively solving problems during class time. When you ask students to explain their reasoning while comparing answers with other students in groups, you are making explicit the steps of checking one’s answer and revisiting problems after they are solved. In class, you can monitor students’ processes, offer hints, and demonstrate that difficulties and wrong answers are just steps along the path to solving hard problems. Even if your course enrollment is very large, you can still form groups of students that are able to hear one another and offer peer support. Active learning has been shown to dramatically decrease achievement gaps between black and white students [1].

Effective teaching means students are engaging in sense-making, rather than simply following procedures or worse, watching an instructor follow procedures. In online classes this year, I asked challenging conceptual questions using the Poll Everywhere website. For example, I asked which was most likely to be Poisson distributed: the time until the next customer arrives, the time until a user logs off a website, the number of students who arrive to class today, or the number of patients who arrive at an emergency room between midnight and 6 a.m. Questions like these are difficult enough that only about a third of the class chose the right answer, with other wrong answers being about equally popular. Then, I put students in groups and asked them to try to harmonize their answers, using resources including the textbook or their notes. Students worked together to clarify the relationship between the exponential and Poisson distributions.

Plenty of evidence establishes the increased effectiveness of collaborative and conceptual approaches to teaching [3].

Anti-racist Teaching = Good TeachingUltimately, anti-racist teaching is really just good teaching; shouldn’t all instructors aspire to reach every single student in their classroom? The mindset that courses should be expected to weed students out is exclusionary and has racist impacts. I have replaced the outdated idea of selecting students who can succeed in a competitive and unsupportive environment with the goal of designing an O.R. classroom that encourages growth, cooperation, excitement and belonging for all students. Of course, as operations researchers we also have exceptional opportunities to directly model considerations of equity in our optimization objectives, models and analyses.

I am indebted to a whole community of anti-racist educators who generated and shared the ideas and tactics I have discussed here. I acknowledge I might have huge gaps in my understanding of anti-racist teaching practices, and I welcome feedback and correction from anyone. Still, we must start somewhere. I urge all instructors to take concrete steps to ensure equity in their courses, even though these types of changes require substantial and sustained effort. You can also keep growing your capabilities by attending presentations and reading resources such as those created by the Center for Minorities in the Mathematical Sciences, Mathematical Sciences Institutes Diversity Initiative of the NSF, National Academies Press and others [4].

SOMMER GENTRY ([email protected]) is a professor of mathematics at the United States Naval Academy and a research associate in surgery at the Johns Hopkins School of Medicine.

REFERENCES1. National Academies of Sciences, Engineering, and Medicine, 2016,

“Barriers and Opportunities for 2-Year and 4-Year STEM Degrees: Systemic Change to Support Students’ Diverse Pathways,” Washington, D.C.: The National Academies Press, https://doi.org/10.17226/21739.

2. Treisman, U., 1992, “Studying Students Studying Calculus: A Look at the Lives of Minority Mathematics Students in College,” The College Mathematics Journal, Vol. 23, No. 5, pp. 362-372, https://www.jstor.org/stable/2686410.

3. National Research Council, 2015, “Reaching Students: What Research Says About Effective Instruction in Undergraduate Science and Engineering,” Washington, D.C.: The National Academies Press, https://doi.org/10.17226/18687.

4. Sathi, V. and Hogan, K.A., 2019, “Want to Reach all of Your Students? Here’s How to Make Your Teaching More Inclusive,” The Chronicle of Higher Education, July 22, https://www.chronicle.com/article/how-to-make-your-teaching-more-inclusive.

Op-ed: Anti-racism

In class, you can monitor students’ processes, offer hints, and demonstrate that difficulties and wrong answers are just steps along the path to solving hard problems.

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Taxonomy for academic institutions’ programs

and course offerings

by

DURSUN DELEN

for TeachingBetter Practices

Business Analytics

MAINLY FUELED BY THE DEMAND FROM industry, analytics and data science have emerged as new and exciting subjects for higher education institutions, helping them to become more real-world and business-need-oriented by bridging the gap between academic offerings and industry demands. The number of current and expected future jobs in analytics and data science far exceeds what is offered by universities. Facing this unprecedented demand (posed by the job market) for educating the next generation of analytics and data science professionals should be perceived by universities as a requirement, a mandate and an opportunity that they cannot afford to pass. As a result, most institutions have already developed, or are in the process of developing, degree and/or certificate programs in analytics and data science.

Teaching business analytics is a challenging endeavor due to its remarkably broad scope (i.e., coverage of disciplines and knowledge areas, such as databases, information systems design, decision modeling, statistics, machine learning, etc.), as well as its emphasis on building both technical (e.g., data wrangling, feature engineering, modeling building, low-level programming, etc.) and behavioral skills (e.g., knowledge elicitation, problem identification, project management, stakeholder communication, etc.) [1, 2]. The challenges are especially significant when teaching analytics in a business school to a less-technical student body majoring in a variety of business subject areas.

As an emerging, rapidly developing and highly exciting subject area, analytics seems to have many moving and unsettled parts. Based on who you ask, it may be called by different names, associated with diverse concepts, and have dramatically varying definitions. This article aims to demystify analytics and data science definitions by providing a longitudinal view of its evolving terminology; provide the most prevailing forces behind the popularity of analytics from both industry and academic perspectives; structure analytics and data science teaching using a simple taxonomy; and provide a logical sequence of potential courses to offer. Software tools for teaching analytics with ease will also be discussed.

Demystifying Business Analytics TerminologyThere is no universally-accepted definition for analytics – some are broad and inclusive; some are narrow and focused on certain capabilities. Although the scope of these definitions may be

seemingly different, the underlying theme and purpose are the same, which is to turn data into actionable insight to make better and more timely decisions [3]. Often depicted as a process, analytics uses the enablers of many disciplines (led by statistics, machine learning and operations research) in this scientific journey of discovering new and novel knowledge for solving previously deemed unsolvable problems. Simply put, analytics is the art and science of marrying data and math to solve problems and make optimal decisions.

Over the years, analytics has taken on different names and “buzzwords,” therefore the proper understanding of the underlying phenomenon warrants some delineation. Among these terms are business intelligence, business analytics, data science and artificial intelligence (AI) [4]. In addition, terms such as big data, machine learning, Internet of Things (IoT), natural language processing, among others, are more the enablers of analytics. Figure 1 attempts to propose some structure to the underlying complexity of these terms, using a relatively simple Venn diagram.

To better portray the name changes over time, a timeline is provided in Figure 2, where the progressive terminology used to describe analytics since the 1970s is depicted. The proposed changes in the terminology are portrayed at the top, while corresponding enabling technologies are listed at the bottom.

Among all of these terms, the two that are most often used interchangeably are business analytics and data science. Are they the same? If so, why do we have two different names for the same concept? For this article’s purpose, these terms are the same; both aiming to transform data into information and actionable insights through an algorithm-based discovery process. The differences are usually observed at the scope dimension. Similar to the scope enlargement we witnessed moving from business intelligence to business analytics, we are now experiencing yet another scope enhancement moving from business analytics to data science. While business analytics adds predictive and prescriptive modeling on top of business intelligence, data science adds big-data enablers, low-level programming (e.g., Python, R, SQL/ NoSQL, JavaScript, Perl), and advanced machine learning (i.e., deep learning and its variants) [5]. Another difference can be observed in each application domain and the tools employed; business analytics deals with business problems

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40

using workflow-type tools and readily available algorithms, and data science deals with a broader range of problems using low-level programming and enhanced algorithm and custom solution development processes.

Furthermore, there are observed differences in the use of these two terms – business analytics and data science – in higher education degree programs. Business schools offering certificate and graduate/undergraduate degree programs typically title their offerings business analytics, while computer science, statistics and industrial engineering schools are offering similar programs under the name of data science. A quick look at these curricula reveals that while business analytics programs are more applied, problem-solving and high-level methodology focused, data science programs focus on programming, algorithm development and low-level methods. While these observations are generally valid, they are not universally applicable, standardized or uniformly practiced.

Driving Forces Behind Business Analytics ProgramsAfter more than a decade, the popularity of analytics-related degree and certificate programs in academia only continues to increase. In recent years, we have witnessed the advent of hundreds of educational programs in the United States, Europe, Far East and other countries by both degree-awarding, well-established universities and non-degree focused (mostly offering online, asynchronous classes), newly created educational

platforms [6]. This increased interest in analytics programs is mostly driven by the continually growing demand from employers [7].

There are myriad articles about analytics and how analytics is destined to change the world by changing the way managerial decisions are made. Analytics has become a new label for evidence-based management (i.e., evidence/data-driven decision-making) [8]. All types of organizations are interested in employees equipped with analytics skills. But why has analytics become so popular in the industry and subsequently in academia? And why now? The reasons (or forces) behind this popularity can be grouped into three main categories: need, availability and affordability, and culture change [4, 7].

Need for better decisions. Conducting business is anything but “as usual” nowadays. Competition has been progressively transformed from local to regional, to national, and now, global. The protections created by tariffs and logistic costs that sheltered companies in their geographic regions are no longer as prominent as before. In addition to increased global competition, and perhaps because of it, customers have also become more demanding – asking for the highest quality products and services at the lowest prices and fastest delivery. Therefore, the need for fact-based, accurate/optimized and timely decisions is more critical than ever before. Analytics is promising to provide managers with the insights they need to make better and faster decisions.

Availability and affordability of enablers. Thanks to recent technological advances and the affordability of software and hardware, organizations are collecting tremendous amounts of data. IoT data collection systems – based on a variety of sensors and RFID technologies, internet and social media sources – have significantly increased the quantity and quality of data. In addition to the ownership model, cloud solutions and software- (or hardware-) as-a-service business models allow businesses (especially small to medium-sized businesses with limited financial power) to lease analytics capabilities and pay only for what they use [9].

Culture change. There has been a shift from traditional, intuition-driven decision-making to contemporary, evidence-based decision-making at the organizational level. Most successful organizations have made a conscious effort to shift to data-driven business practices. Because of the availability of data and supporting information systems infrastructure, such a paradigm shift is taking place faster than many thought possible. As the new generation of quantitatively savvy managers replaces the baby boomers, this evidence-based managerial paradigm is expected to accelerate.

A Taxonomical View to Characterizing and Teaching Business AnalyticsAs interest in adopting analytics grows, the need for a better definition and characterization of the underlying concepts into a simple taxonomy has emerged. Such a taxonomical structure could create

a common understanding and definition for its practitioners, eliminate myths and misconceptions, and provide a framework for developing more effective analytics curricula.

About a decade ago, several well-known consultancy companies (e.g., IBM, SAS, Gartner, Accenture, IDT, among others) and international institutions embarked on a mission to create such a simple taxonomy for analytics. One of the institutions that embarked on this journey was the Institute for Operations Research and the Management Sciences (INFORMS). To gain a broad perspective of industry and academia and a holistic representation of the underlying concepts, INFORMS hired Capgemini, a strategic management consulting firm, to conduct a study to define and characterize analytics, and potentially create a simple taxonomy [10]. The study proposed a common definition for analytics wherein they identified three consecutive and somewhat overlapping levels/echelons: descriptive, predictive and prescriptive.

Descriptive analytics is the initial stage in the analytics continuum. It is often called business reporting because most of the analytics activities at this level deal with creating on-demand reports to summarize business activities to answer, “What happened?” and “What is happening?” An extension of descriptive analytics, often titled diagnostic analytics, is also part of this taxonomical representation. It is worth noting that some depictions of analytics taxonomy show diagnostic analytics (which aims to answer the question “Why did it happen?”) as

AI & DecisionAutomation

DataScience

BusinessAnalytics

BusinessIntelligence

Big Data Operations Research

Machine LearningRFIDIoTSensors

Text Mining/NaturalLanguage Processing

Procedural LanguagesOptimization & Simulation

AI & Expert Systems

Object Oriented ProgrammingRelational Database Systems

Enterprise Resource Planning

Data WarehousingData Visualization

Multi-tiered Architectures

In-memory & In-databaseData/Text/Web Mining

AI/Deep LearningSocial Media & IoT

Deep[er] LearningAutoML & XAI

Evolution of the Enablers (Tools and Technologies)

Task AutomationPeriodic ReportingDecision Support Systems

Integrated Information SystemsOn-demand Reporting

Enterprise Integration

Enterprise Information SystemsDashboards & Scorecards

Executive Information Systems

Evidence-based ManagementBusiness Analytics

Data Analytics & Data ScienceBig Data Analytics

Autonomus DecisioningSmart Robo-assistants

Driven by Academic Initiatives,

Theories and Applications

Driven by Industry Trends,

Practices, Needs and Wants

1970s

Decision Support Systems

1980s

Enterprise Support Systems

1990s-2000s

Business Intelligence

2015s

Big Data

2010s

Analytics

2020s+

Automation

FIGURE 1: Relationships among the popular terms and concepts in analytics. FIGURE 2: A longitudinal view of the naming of analytics-related trends and tools.

Teaching Business Analytics

Academic institutions can use this simple taxonomy to streamline their certificate and degree program offerings in a logical and proven manner.

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a separate echelon in between descriptive and predictive layers. However, the original three-tier depiction, where diagnostic is an extension of descriptive analytics, is more widely accepted. Descriptive analytics is also called business intelligence, and predictive and prescriptive analytics together are called advanced analytics.

Predictive analytics follows descriptive analytics, where the goal is to look beyond what happened and try to answer the question, “What will happen?” Using a variety of forecasting and machine-learning techniques, predictive analytics aims to make scientific estimates about the future values of any variables of interest. If what is being predicted is a categorical variable, the act of prediction is called classification; otherwise, it is called regression. If the predicted variable is time-dependent, the prediction process is often called time-series forecasting.

Prescriptive analytics is the last tier in the analytics continuum, where the best alternative among many courses of action (identified through predictive and descriptive analytics) is determined using logical and mathematical models. It aims to answer, “What should I do?” by utilizing O.R. methods (e.g., optimization, simulation and heuristics-based decision-modeling techniques). The methods behind prescriptive analytics are not new; most of the underlying methods were developed in the 1940s during World War II when there was a dire need to achieve the best results using limited available resources.

Academic institutions can use this simple taxonomy to streamline certificate and degree program offerings in a logical and proven manner (see sidebar). The survey of current academic offerings shows that there is a wide variety of curriculum configurations adopted by universities, colleges and academic departments. Some curricula are more technical, programming and algorithm focused, while others are more managerial, problem and implication focused. This is expected because departments and colleges desire to teach what they think their students should know to be competitive in the job market.

Another reason for such disparity in analytics offerings is driven by the lack of time and proper teaching resources. Because of the rapidly emerging popularity of analytics in business circles, academic departments were asked to respond to the demand by quickly designing and offering analytics-related certificate and degree programs, and were often asked to do this without adding any new faculty. Therefore, instead of taking a foundational and revolutionary approach to design the most appropriate degree programs with new courses, institutions transformed most of their existing course offerings under analytically flavored titles with minimal change in content. This practice of repackaging existing courses leads to less than ideal configurations of analytics programs.

What Tool(s) to Use for Analytics Teaching?Parallel to the tremendous growth in analytics and data science, the landscape of software tools that can be used to apply these technologies is also evolving and expanding at an unprecedented pace [7]. There already exist numerous software tools to use for analytics and data science, ranging from commercial and paid to free, open-source tools, from visual/workflow-type to programming language-based platforms. Some software tools are cloud-based, while others require local installations, and yet some provide local installation and complement it with cloud-based platforms for computational efficiency and ease of model deployment. Tools vary in terms of data preprocessing and flexible and powerful model building, as well as ease of learning, use and deployment.

Although Python and R have emerged as “have it all” programming platforms for analytics and data science, they also have their shortcomings, largely related to their broad and unverified collection of sources/libraries and over-reliance on syntactic programming structures. Most business analytics and data science professionals tend to use a portfolio of software tools and programming languages collectively and synergistically to produce the best possible outcomes for a given data science project. Mathematical and statistical knowledge of the underlying algorithms, experience with the most effective analytics platforms, domain knowledge and people management skills are all expected components for a superior data scientist [5]. In addition, the success of a data scientist also depends on the tools they rely on. For a successful business analytics education and subsequent employment, a good mix of knowledge and skills in popular analytics programming languages and workflow- type analytics software tools (e.g., SAS EM, IBM Modeler, KNIME, Alteryx) is necessary.

Due to the multifaceted, multidisciplinary nature of analytics, students might be hesitant to pursue a degree in it, especially if their background does not include technical or mathematical skills and knowledge. To ease entry into this seemingly technical field, that is, to make analytics learning (and teaching) easy (or easier), software-related complexities can (and should) be remedied by using a heavy dose of visual and intuitive model development platforms [11]. Having to spend less mental energy on the syntactic details of the tool could allow students and teachers to focus on the foundational concepts and best practices of analytics. There are many visual tools in the marketplace; most of them are commercial. KNIME Analytics Platform is a free and open-source visual software tool that fits very well with this teaching philosophy [12].

KNIME Analytics Platform offers one of the most comprehensive data access and processing capabilities, model building and testing functions (including ordinary machine learning as well as deep learning

algorithms), data visualization and model deployment options. Although the initial installation comes with more than 2,000 native nodes (i.e., functions embedded in visual icons), if needed, one can build new nodes, use those built by the KNIME community, or integrate with the functionalities of scripting languages like Python and R.

Summary and ConclusionLargely driven by the need to make data-driven, better and faster decisions, availability of data in large volumes and varieties, along with the advances in analytics methods and methodologies provide an unprecedented opportunity for universities to produce graduates who can address highly challenging and very interesting real-world questions. Analytics and its derivative terms have emerged as the new paradigm to shift problem- solving and decision-making from traditional practices (relying on gut feelings and limited past experiences) to modern-day executions (relying on data, evidence and mathematical sciences). Higher education institutions are now tasked to produce this new generation of decision-makers who can face today’s complex business challenges with precision and confidence.

DURSUN DELEN, Ph.D., is the William S. Spears Endowed Chair in Business Administration, Patterson Family Endowed Chair in Business Analytics, director of research for the Center for Health Systems Innovation, and Regents Professor of management science and information systems in the Spears School of Business at Oklahoma State University.

REFERENCES 1. Ceccucci, W., Jones, K., Toskin, K. and Leonard, L., 2020,

“Undergraduate Business Analytics and the Overlap with Information Systems Programs,” Information Systems Education Journal, Vol. 18, No. 4, pp. 22-32.

2. Clayton, P. R. and Clopton, J., 2019, “Business Curriculum Redesign: Integrating Data Analytics,” Journal of Education for Business, Vol. 94, No. 1, pp. 57-63.

3. Delen, D., 2019, “Prescriptive Analytics: The Final Frontier for Evidence-Based Management and Optimal Decision Making,” Pearson, FT Press Analytics: Upper Saddle River, N.J.

4. Delen, D., 2021, “Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners,” Pearson, FT Press Analytics: Upper Saddle River, N.J.

5. Johnson, M. E., Albizri, A. and Jain, R., 2020, “Exploratory Analysis to Identify Concepts, Skills, Knowledge, and Tools to Educate Business Analytics Practitioners,” Decision Sciences Journal of Innovative Education, Vol. 18, No. 1, pp. 90-118.

6. Paul, J. A. and MacDonald, L., 2020, “Analytics Curriculum for Undergraduate and Graduate Students,” Decision Sciences Journal of Innovative Education, Vol. 18, No. 1, pp. 22-58.

7. Sharda, R., Delen, D. and Turban, E., 2020, “Analytics, Data Science, & Artificial Intelligence: Systems for Decision Support,” 11th ed., Pearson: New York.

8. Davenport, T. H., 2018, “From Analytics to Artificial Intelligence,” Journal of Business Analytics, Vol. 1, No. 2, pp. 73-80.

9. Delen, D. and Demirkan, H., 2013, “Data, Information and Analytics as Services,” Decision Support Systems, Vol. 55, pp. 359-363.

10. Robinson, A., Levis, J. and Bennett, G., 2010, “INFORMS to Officially Join Analytics Movement,” OR/MS Today, October.

11. Hu, Y., Chen, C. H. and Su, C. Y., 2021, “Exploring the Effectiveness and Moderators of Block-based Visual Programming on Student Learning: A Meta-analysis,” Journal of Educational Computing Research, Vol. 58, No. 8, pp. 1467-1493.

12. Silipo, R., 2020, “Why KNIME?” on Medium Blogging Platform, https://medium.com/swlh/why-knime-98c835afc186 (accessed July 2021).

A STRUCTURED COURSE SCHEDULE FOR BUSINESS ANALYTICS

The following course structure is designed and proposed based on the author’s 30+ years of experience as a consultant, university instructor and advisor, analytics textbook author and mentor to several institutions in the U.S. and abroad. Given the genuine interest to develop the best possible curriculum and the availability of needed resources, a business analytics or data science (BA/DS) graduate degree program can be designed as follows.

Preamble • An overview class to introduce BA/DS as the new way of making

smart managerial decisions. A motivational overview of underlying concepts and topics with interesting high-level case studies. It may also include flavors of the human decision-making processes (Simon’s model from the 1980s).

• An introduction to analytics programming class (R or Python, if desired).

Descriptive Analytics • A class on the nature of data and simple statistics (primarily

covering descriptive statistics and some inferential statistics).• A class on SQL, relational databases and data warehousing. • A hands-on class on data visualization insights and best

practices using any combination of Excel, Tableau, PowerBI and SAS Visual Analytics.

Predictive Analytics • Data mining: covering the process and different pattern recognition

methods (prediction, including time series prediction, clustering, association, network modeling/mining).

• Text mining: web and social media mining (both content and structure perspective).

• Machine learning for predictive analytics: a best-practices-driven approach to cover the concepts and application of a rich collection of machine-learning techniques.

• Advanced analytics: big data, deep learning, explainable AI, AutoML and cognitive computing.

Prescriptive Analytics • Optimization modeling (linear and nonlinear): the fundamental

concepts of mathematical modeling paired with Excel Solver hands-on experiences.

• Simulation and heuristic modeling: basic concepts of simulation modeling (e.g., Monte Carlo simulation) and heuristic search methods (e.g., genetic algorithms) with Excel-based exercises.

• Multicriteria decision modeling and group decision-making basics with simple and intuitive hands-on exercises.

Wrap-up • An analytics project-management approach to executing BA/DS

projects, identifying and blending the best practices in BA/DS and PM for a better (on-time, on-budget, on-target) delivery of actionable results/insights.

• A capstone class involving end-to-end solution development for a challenging real-world problem using real data. Preferably, such a project would come from a real company, perhaps under an internship-type engagement.

Based on characteristics of the analytics offering at each academic department, additional, subject-oriented courses can be added, combined/merged, or some of the courses can be split into more specific ones.

Teaching Business Analytics

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QUANTUM COMPUTERS USE THE LAWS OF quantum mechanics to manipulate information. The corresponding model of computation can be faster than any classical computer for certain tasks. (See some introductory material [1, 2] and a comprehensive reference [3].) For several decades, researchers – mostly in theoretical computer science and quantum physics – have been studying the quantum computing model and investigating what can be done with it. Recent hardware developments have spurred a tremendous surge of interest from the government, industry and academia; see [4] for a discussion of the current state of quantum computing hardware, possible applications and the challenges that lie ahead. This interest has prompted institutes of higher education to explore what a quantum computing curriculum should look like. This article discusses the role that operations research and management science (OR/MS) disciplines can play in the education of quantum computing scientists.

Interface Between OR/MS and Quantum ComputingQuantum computing (QC) is considered a branch of physics or computer science, especially the parts that concern algorithms. At the same time, some of the main application areas of QC for scientific computation, such as optimization and simulation,

are traditionally within the domain of OR/MS. The main reason for this apparent disconnect is probably the lack of available quantum computers up to this point: OR/MS is a field rooted in practical problems and numerical evaluation, whereas quantum algorithms have – for the most part – remained purely theoretical.

With recent progress in QC hardware, companies (e.g., IBM, Rigetti Computing, D-Wave), academic institutions (e.g., Berkeley Labs through their AQT initiative) and government institutions (e.g., Sandia National Laboratories through their QSCOUT initiative) are making their QC hardware available to the public. Thus, it is time for the OR/MS community to start looking at QC as a model of computation that could have practical applications in a few years. Not only can QC algorithms be developed for OR/MS applications, but OR/MS techniques can also be used to improve existing QC methods (e.g., classical optimization is an important component of many QC algorithms for near-term QC devices).

Further intersections arise in key areas of QC related to noise mitigation, embedding of quantum circuits, and development of new QC algorithms, to name a few. This interaction may lead to new quantum-inspired methodologies that address OR/MS problems using classical computation; for example, quantum-inspired algorithms have been proposed in areas such as linear algebra, support

by GIACOMO NANNICINI, SWATI GUPTA, SVEN LEYFFER,

JIM OSTROWSKI and LUIS F. ZULUAGA

OR/MS Education and Quantum Computing

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vector machines and heuristic optimization methodologies. For the intersection of QC and OR/MS to thrive, we must provide students with the skills to excel in these fields.

QC Skills for the Job MarketQC encompasses many subareas, each of which focuses on specific aspects. It would be impossible to list all of them; therefore, we limit ourselves to a few areas that have a large overlap with OR/MS. Note that we only discuss the intersection with OR/MS, refraining from giving an exhaustive description of the skills required in each area.

• Quantum algorithms: This area requires a strong training in linear algebra, discrete mathematics (including graph theory), probability and computational complexity. Quantum algorithms are generally taught separately from classical algorithms due to the important differences in the computational model. Because optimization, simulation and machine learning are highly sought-after application areas, training in these disciplines is extremely valuable. Understanding quantum physics is not strictly necessary to work on algorithms, although it may help, especially since many efficient quantum algorithms concern the simulation of quantum mechanical systems.

• Hardware design and control: This area requires training in data analytics (statistics, machine learning), probability and optimal control. Optimization and modeling skills are also desirable and can be very useful in tackling new challenges that arise in hardware design.

• Quantum software development: Writing quantum software requires knowledge of linear algebra, discrete mathematics, computational complexity and programming languages.

The importance of these subareas is based on the personal experience of the authors, as well as a survey conducted on companies that recruit in the area of QC [5]. Specifically, the study [5] focuses on 21 companies of different sizes and with different areas of focus that belong to the Quantum Economic Development Consortium (QED-C) (see Figure 1 in [5]). The study found that 57% of the reviewed companies hire students with a bachelor’s degree in engineering, while 38% hire students with Ph.D. degrees in engineering (a high number of students that pursue graduate studies in OR/MS do so after graduating from a B.Sc. in engineering, or pursue the graduate program in an engineering department, such as industrial engineering).

Although the area of OR/MS is not explicitly mentioned in the study, its results show that students going into a graduate program in OR/MS from a more purely mathematical or computational science background also have plenty of opportuni-ties in the quantum industry. In particular, the study shows that 24% of the companies hire students with a Ph.D. in computer science, and 10% hire students with a Ph.D. in mathematics (see Figure 3 in [5]), two areas that overlap with certain concentrations of Ph.D. degrees in OR/MS.

One of the most important conclusions of this study for students in OR/MS that are looking to get involved in the quantum industry is companies are not necessarily looking only for experts in QC and/or quantum information. The quantum industry is also looking to fulfill positions in which a deep knowledge of the theory behind quantum information science is not a necessary or sufficient requirement. Indeed, classical skills are highly valued as well (see Figure 1).

A second study performed by the QED-C, with responses from 60 companies in the quantum industry, provides a more granular view. A summary

is reported in Figure 2, the original data is discussed in the videos available at the NSF Workshop on Quantum Engineering Education [6]. As before, the conclusion is that many sought-after skills are typically taught and developed in OR/MS curricula, e.g., modeling and simulation, machine learning, statistics and business development.

While this discussion is not exhaustive, we hope it provides an overview of the skills that should be trained to be successful on the job market, and this in turn should inform the development of school curricula. To get a sense of available positions, we provide two useful resources. The first is the database maintained by the Quantum Economic Development Consortium (QED-C) [7]. As of June 2021, the list included 369 entries from companies such as Amazon, Google, IBM, D-Wave, IonQ, Microsoft, QCWare and Rigetti, among others. The second is the newsletter produced by the ORNL Quantum Computing Institute, which not only contains job openings (hundreds of them in the June 2021 newsletter, plus directions to 245 company and university career sites with multiple open positions), but also a summary of news in the area, such as funding opportunities and calls from conferences [8].

OR/MS Courses in QC EducationDue to the vastness of the field of QC, as well as its inherent interdisciplinarity, a course of study in QC is unlikely to be offered by a single department. This is especially true at the undergraduate level, where the basis for the discipline has to be established. At the graduate level, where courses are more specialized, such an endeavor is possible. The prototypical QC degree includes foundational classes in linear algebra, QC and quantum information science, computer programming (at least classical), and is likely to include at least some classes in quantum technologies (i.e., the basic principles underlying

quantum hardware). These classes are unlikely to be offered by an OR/MS department, although linear algebra is often a prerequisite in OR/MS curricula.

The contributions of OR/MS to quantum education begin to shine when elective classes are considered. What follows is a nonexhaustive list of courses that could be offered as electives in a QC curriculum, and that may in fact be required classes for certain concentrations.

• Modeling and simulation: Modeling skills are at the heart of OR/MS and find multiple applications in QC. These skills are required for quantum application researchers and developers; quantum algorithms for simulation [13] have been developed, therefore expanding them and applying them to real-world problems is an important task for this job role. They are also required for noise characterization in quantum hardware.

• Optimization: Optimization is one of the main areas being investigated for quantum applications; it is also pervasively used in many branches of business and science. In addition to quantum algorithms for optimization, optimization is a frequently used subroutine for some quantum algorithms for near-term quantum devices, and is used in quantum information science, making it a required skill for quantum application researchers and developers. Hardware design problems can also benefit from optimization.

• Machine learning and data analytics: Multiple experimental tasks (noise characterization, tuning of devices) employ these skills, which are also required for many quantum application researchers and developers. The area of quantum machine learning is very active and receives significant attention from the industry, developing at rapid pace.

Skills/knowledge most sought after (first survey)Quantum algorithms

Materials

Troubleshooting and problem solving

Electronics

Laboratory experience

Statistical methods for data analysis

Coding

Skills

and k

nowl

edge

Percentage of companies valuing that skill0 10 3020 40 50 60 70 80 90 100

80

60

40

20

0

Skills/knowledge most sought after (second survey)

Perce

ntag

e of c

ompa

nies v

aluing

that

skill

Product development

Software development

Modeling or simulation

Quantum science

Quantum algorithm development

Computer science & engineering

AI/ML algorith

m development

Device testin

g & characteriza

tion

Electronics

Quantum sensor physics

Systems arch

itecture

Analog circuit d

esign

Noise measurement & analysis

Applications d

esign

Circuit o

r syste

m testing

Business d

evelopment

Theoretical m

athematics or st

atistics

Cryogenic c

omponents & sys

tems

Device physic

s

Digital circuit d

esign

Sales & marke

ting

Quantum photonic or la

ser physics

FIGURE 1: Use of highly desired skills in the quantum industry – first dataset.

FIGURE 2: List of highly desired skills in the quantum industry – second dataset with granular data.

Quantum Computing Education

It is time for the OR/MS community to start looking at QC as a model of computation that could have practical applications in a few years.

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• Probability: This should be a required course for all engineering-type degrees in quantum computing, due to its ubiquity.

• Project management: Several job roles in the quantum industry related to management and business development require traditional management science skills such as project management.

To get introduced to some of the existing capabilities of quantum computers in these areas, as well as the corresponding research challenges, some comprehensive reports and the references therein are an excellent starting point to find elements of QC that can be incorporated into the above classes [9, 10]. The NSF workshop on quantum engineering education website [6] also contains several resources regarding QC education (an extensive report on QC undergraduate education, being prepared by the QED-C, is likely to be available in the near future).

Programming LanguagesThere are plenty of online resources to understand the available quantum programming languages and their functionality. Major companies working in the field have developed their own languages and tools, and most of them come with learning resources. Programming languages tend to be heavily Python-based, so working knowledge of Python is often a requirement. Quantum code (usually in the form of circuits) can be tested using local simulators, which work for a restricted number of qubits on normal laptop computers – but more than enough qubits to test and learn. Access to quantum hardware is available via the cloud from various companies and national laboratories – free of charge in some cases. Excellent entry points for a discussion on quantum computing languages are the technology column [11] and the more detailed survey [12].

In conclusion, we believe that this is an excellent time to invest in quantum engineering education. Given the surging demand from the industry, it is vital that we equip the workforce of tomorrow with the analytical background to adapt to new technical challenges. The intersection of the classical and quantum model of computing presents many beautiful problems that OR/MS disciplines can address if we teach students the relevant skills.

Acknowledgments: The INFORMS Computing Society formed a working group on quantum computing in November 2020. This article is written by the members of the working group.

GIACOMO NANNICINI ([email protected]) is a research staff member at IBM Quantum, IBM T.J. Watson Research Center.

SWATI GUPTA ([email protected]) is a Fouts Family Early Career Professor and assistant professor at H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology.

SVEN LEYFFER ([email protected]) is a senior computational mathe-matician in the Mathematics and Computer Science Division, Argonne National Laboratory.

JIM OSTROWSKI ([email protected]) is an associate professor and director of graduate studies in the Department of Industrial & Systems Engineering, University of Tennessee.

LUIS F. ZULUAGA ([email protected]) is an associate professor in the Department of Industrial and Systems Engineering at Lehigh University.

REFERENCES AND NOTES1. Nannicini, G., 2020, “An introduction to quantum computing, without

the physics,” SIAM Review, Vol. 62, No. 4, pp. 936-981.2. Rieffel, E. and Polak, W., 2000, “An introduction to quantum computing

for non-physicists,” ACM Computing Surveys (CSUR), Vol. 32, No. 3, pp. 300-335.

3. Nielsen, M.A. and Chuang, I., 2002, “Quantum computation and quantum information,” Cambridge University Press.

4. Preskill, J., 2018, “Quantum computing in the NISQ era and beyond,” Quantum, Vol. 2, p. 79.

5. Fox, M.F., Zwickl, B.M. and Lewandowski, H., 2020, “Preparing for the quantum revolution: What is the role of higher education?” Physical Review Physics Education Research, Vol. 16, No. 2, Article no. 020131.

6. NSF Workshop on Quantum Engineering Education, https://www.osa.org/en-us/meetings/topical_meetings/quantum_engineering_education_workshop/.

7. Quantum Economic Development Consortium (QED-C) jobs database, https://quantumconsortium.org/quantum-jobs/.

8. ORNL quantum computing mailing list, https://elist.ornl.gov/mailman/listinfo/qci-external.

9. Aspuru-Guzik, A., Van Dam, W., Farhi, E., Gaitan, F., Humble, T., et al., 2015, ASCR Workshop on Quantum Computing for Science. Technical report, Sandia National Lab, Albuquerque, N.M.

10. Wolf, S.A., Joneckis, L.G., Waruhiu, S., Biddle, J.C., Sun, O.S. and Buckley, L.J., 2019, “Overview of the status of quantum science and technology and recommendations for the DoD.” Technical report, Institute for Defense Analyses, Alexandria, Va.

11. Matthews, D., 2021, “How to get started in quantum computing,” Nature, Vol. 591, pp.166-167.

12. Heim, B., Soeken, M., Marshall, S., Granade, C., Roetteler, M., et al., 2020, “Quantum programming languages,” Nature Reviews Physics, Vol. 2, No. 12, pp. 709-722.

13. This refers to event simulation and estimation of probabilities; not to be confused with Hamiltonian simulation, a fundamental problem in QC for which efficient quantum algorithms exist.

48

Quantum Computing Education

What’s Your StORy?Sommer GentryProfessor of Mathematics at the United States Naval Academy, and Research Associate, Department of Surgery, Johns Hopkins University School of MedicineINFORMS member since 2001

Apart from the individual INFORMS groups and networking, what is your favorite member benefit?I love having access to the journals. I use INFORMS Journal on Applied Analytics the most. I teach operations research to undergraduates at an undergraduate-only campus and we have an O.R. major, which is a great opportunity for our students. I like to use papers from there to inspire and give my students guidance on the models that they’re using.

Would you say the work you do saves lives, saves money, or solves problems? Which one(s) and how?First and foremost the work I do is focused on saving lives, and it is really rewarding both to be able to explain math to a transplant audience and explain to my students that they can make the kind of difference that would rescue someone. I actually decided early on that I never wanted to work on a problem where the objective function was dollars – so I have not; I have only worked on scientific problems trying to understand what’s true, trying to understand what policies help people.

Tell us some pros and cons about virtual and in-person conferences.In virtual conferences it’s easier to switch to a different talk or session within the 1-hour period; you can watch just what you’re interested in and don’t have to physically change rooms.

I really liked networking in the setting of the virtual meeting because I felt that people would sit down and talk with you, and it was less distracting an environment than if you’re trying to have a conversation with someone in the physical space at the conference. Most people would assume that you get less networking opportunities in a virtual meeting, but I found it was more, because I could actually spend time talking to somebody and getting to know them and they weren’t distracted.

Read Sommer’s op-ed “Anti-racist Teaching Practices” in this issue (page 34).


by

FREDERICK S. HILLIER

EYEWITNESS ACCOUNT OF THE ILLUSTRIOUS HISTORY OF OPERATIONS RESEARCH AT

STANFORD UNIVERSITY

Faculty of O.R. heroes produces major contributors to the field – a veritable who’s who of OR/MS.

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a

67-YEAR


53INNOVATIVE EDUCATION ISSUE I ORMSTODAY.INFORMS.ORG I AUGUST 2021

O.R. at Stanford

OVER THE LAST 67 YEARS, I HAVE HAD THE great privilege of both observing and participating in the glorious history of operations research (O.R.) at Stanford University. Sharing this journey with numerous gifted colleagues and students has been a wonderful experience. I record below the highlights of this journey.

1954-1961: Integrating O.R. coursesWhen I arrived at Stanford as a freshman in September 1954, operations research was still a very young field. Only the most progressive universities were beginning to introduce O.R. into their curriculum – Stanford was one of them. A new course, Introduction to Operations Research, was being offered in the Department of Industrial Engineering (IE). This particularly interested me because I was planning to major in IE, and this was exactly the kind of course I wanted to take. It was to be taught by Gerald ( Jerry) Lieberman, a new assistant professor with a joint appointment in industrial engineering and statistics. A little later, Harvey Wagner also joined the Stanford faculty as an assistant professor, and he occasionally taught this course. (Both Lieberman and Wagner went on to become prominent members of the international O.R. community and INFORMS.)

During this time period, there were other Stanford faculty members who were starting to teach O.R.-related courses, including Kenneth Arrow (mathematical economics), Samuel Karlin (stochastic processes and applied probability) and Herbert Scarf (inventory theory). These three individuals then jointly conducted foundational research on the mathematical theory of inventory and production. Their results are still used today.

1962-1967: Ph.D. offering and big namesWith the growing prominence of operations research, Stanford was ready to make a big push in this area. An Interdepartmental Program in Operations Research began to offer a Ph.D. in operations research in September 1962. Jerry Lieberman became the chairman of the program. In addition to Jerry, the faculty consisted of a number of other prominent Stanford professors with appointments in closely related departments and who were teaching the courses needed for this program. Harvey Wagner was a particularly active member of the program before leaving Stanford in 1967.

Some junior faculty needed to be hired to help support this program. With my research and teaching interests in queueing theory, integer programming, risk analysis in capital budgeting, etc., I was the first to be hired upon the completion of my Ph.D. in 1961. Arthur (Pete) Veinott (inventory theory, dynamic programming and lattice programming) arrived a year later. (Both of our appointments were in the IE department.) Within a few more years, Donald Iglehart (applied probability and simulation) and Richard Cottle (mathematical programming) joined the staff.

All four of these junior faculty members were destined to become core members of the Stanford O.R. program throughout the next few decades as they gained increasing prominence in the field. All four became INFORMS Fellows and winners of other major awards from INFORMS. (Started in 2002, INFORMS Fellows are a highly selective group of INFORMS members who have been awarded this honor because of their outstanding lifetime achievement in operations research and the management sciences.)

An especially important move during this period was adding George Dantzig, the renowned “Father of Linear Programming,” to the faculty in 1966, who went on to have a huge impact on the O.R. program.

1967-1995: Successful O.R. program becomes department, more big namesStanford’s Interdepartmental Program in Operations Research proved to be a great success, with many outstanding Ph.D. graduates who became major contributors to the field. Therefore, in 1967, this program was converted into a full-fledged Department of Operations Research in the School of Engineering that also would offer an M.S. degree in O.R. and a small number of undergraduate courses. Lieberman continued as the chairman until 1975. He was a natural academic leader and so then moved on to senior administrative positions, including serving as the acting provost or provost under three different Stanford presidents.

Another notable event in 1967 was the publication of the first edition of the “Introduction to Operations Research” textbook, coauthored by me and Lieberman, which became the preeminent textbook on this

topic through a total of 11 editions (including one last year). (See a companion article in the June 2021 issue of OR/MS Today for this story [1].)

With Dantzig joining the faculty, everything was in place for the Stanford Department of Operations Research to enter a golden age. In addition to his research and teaching, Dantzig made a great impact by bringing in a large number of outstanding researchers to complement our program. Using his research funds, he founded the Systems Optimization Laboratory and hired outstanding computer scientists such as Michael Saunders, Walter Murray, Margaret Wright, Philip Gill and John Tomlin to do research in scientific computing and develop O.R. software packages. Dantzig established and led the Energy Modeling Forum; John Weyant was (and still is) a leader in that activity. Others arrived to work with Dantzig in other areas, including Gerd Infanger in the stochastic programming area. Dantzig also supervised many outstanding Ph.D. students, with many impactful research papers (and even a few books) ensuing from his group.

The outstanding contributors to the O.R. program in the initial decades after Dantzig’s arrival extended well beyond George and his collaborators mentioned above. The four junior faculty from the 1960s had become highly productive members of the field. Along with Lieberman, two outstanding mathematical economists, Alan Manne and Arrow (who won a Nobel Prize in 1972) were also founding members of the department. Rudolf Kalman was another founding member, but he left Stanford in 1971. Somewhat later, the department hired two younger faculty – Curtis Eaves and Peter Glynn – who became important longtime members of the department. (Eaves has retired, but Glynn is still a very productive Stanford faculty member who served several years as chairman of the Department of Management Science and Engineering.)

The Department of Operations Research proved to be a real magnet for outstanding students wanting to enter the field. For many years, at least half of the National Science Foundation Fellowship winners going into O.R. each year chose to enter the Stanford Ph.D. program. Those Ph.D. graduates generally went on to become prominent members of the field. Many won the prestigious Fellow Award from INFORMS. Many became deans or senior faculty at top O.R. programs or became prominent practitioners. Many also provided important service to INFORMS and its predecessors.

The department also attracted several dozen students each year for the one-year program leading to an M.S. in operations research. Some of these students were either employees of Bell Labs or members of the military who were sent to Stanford for further education. Additionally, Stanford offered several undergraduate O.R. courses, including a two-quarter sequence that introduced O.R. (using the Hillier-Lieberman textbook) that was largely

taken by students majoring in IE. Enrollment ranged as high as 190. A similar two-quarter sequence with a little more mathematical content was also offered to students in the popular undergraduate major of mathematical and computational sciences.

I introduced an undergraduate course entitled “Models and Applications of Operations Research in Society” (although the faculty privately referred to it as our “O.R. for Poets” course). It had no mathematical prerequisites and was aimed at students in the humanities and social sciences to introduce them to the powerful impact that O.R. could have when addressing societal issues. This course was quite successful, drawing up to 45 students, but was eventually dropped.

Department of Engineering-Economic SystemsThus far, my history of O.R. at Stanford has focused on the Department of Operations Research and its origins. However, there is much more to this story. To start, consider the Department of Engineering-Economic Systems (EES) that was formed in the School of Engineering at about the same time as the O.R. department. The EES department was another outstanding department that revolved around the techniques and application of O.R., but with a more applied orientation. Many of the faculty came out of an electrical engineering background, and the focus was on the analysis of engineering-economic systems. This department also had many distinguished graduates who made an impact on the field. Two pioneering members of the EES faculty (now retired) were particularly renowned contributors to the O.R. field and warrant special mention.

52

The list of where O.R. blossomed at Stanford outside of the Department of Operations Research goes on and on.

Founding members of the new Stanford Department of Operations Research in 1967. (l-r) George B. Dantzig, Alan S. Manne, Frederick S. Hillier, Donald L. Iglehart, Arthur F. Veinott Jr., Rudolf E. Kalman, Gerald J. Lieberman, Kenneth J. Arrow and Richard W. Cottle.


54

O.R. at Stanford

Even before joining the EES faculty, Ronald Howard was a key pioneer in developing the important O.R. technique called Markov decision processes. He then became known as the guru for how decision analysis should be applied and taught. Numerous students (including many from the O.R. department) who took his legendary decision analysis course swear by it. His 2016 textbook with Ali Abbas, “Foundations of Decision Analysis,” is a real gift to the field.

David Luenberger was another prominent pioneering member of the EES faculty. He is a brilliant expositor (Saul Gass Expository Writing Award winner) who has written outstanding multiple textbooks on different topics within the O.R. field. A 1969 book, “Optimization by Vector Space Methods,” is a classic that is still widely cited. Another example is his outstanding textbook, “Linear and Nonlinear Programming,” first published in 1973, but still popular today. (Now retired, Luenberger has added a current faculty member, Yinyu Ye, as a co-author for the last two editions. They now are preparing a new edition.)

Graduate School of BusinessStanford’s Graduate School of Business (GSB) has long been a major center of O.R. activity. Three of the best early Ph.D. graduates of the Department of Operations Research – Michael Harrison (one of my Ph.D. students), Lawrence Wein and David Kreps – joined the GSB faculty a few decades ago. Both Harrison and Wein are INFORMS Fellows who have won several major INFORMS awards, while Kreps has become a prominent mathematical economist. Other GSB faculty have also made special contributions related to O.R. Evan Porteus (another INFORMS Fellow) was a leading researcher in the inventory management and supply chain management areas. William Sharpe won a Nobel Prize in 1990 for using an O.R. type of approach to investment performance analysis. Robert Wilson (a specialist in game theory and its applications in business and economics) was a popular Ph.D. advisor to several students in the Department of Operations Research.

Notably, one of these O.R. department students (mentored by Wilson) was Alvin Roth, who has since gone on to win a Nobel Prize in Economics and is on the Stanford faculty in the Department of Economics. Wilson himself just won the Nobel Prize in Economics in 2020. His co-winner, Paul Milgrom, is also in the Stanford Department of Economics and is affiliated with both GSB and the Department of Management Science and Engineering.

Other Centers of Operations Research ExcellenceAlthough several of its faculty members moved over to the Department of Operations Research in 1967, the Department of Industrial Engineering (later renamed the Department of Industrial Engineering

and Engineering Management) gradually became a major center for research on applications of O.R. During the latter years of the 20th century, the department included three prominent faculty members – Margaret Brandeau, Hau Lee and Elisabeth Paté-Cornell – who became INFORMS Fellows. Brandeau became a prominent leader in what is now the hot area of applying mathematical and economic models to support health policy decisions. (She also has a courtesy appointment as a professor of medicine.) Similarly, Lee (who later moved to GSB in 2002) became a prominent leader in applying O.R. to supply chain management. Paté-Cornell is widely acknowledged to be a leading expert in applying O.R. to risk analysis.

The list of where O.R. blossomed at Stanford outside of the Department of Operations Research goes on and on. O.R. faculty frequently found common ground with faculty in the Department of Statistics, Department of Computer Science, Institute for Computational & Mathematical Engineering, Department of Economics, other departments in the School of Engineering (especially Electrical Engineering), and so forth. For example, the current chair of the EE department, Stephen Boyd, is an INFORMS Fellow whose primary research interests are convex optimization and its engineering applications.

In 1992, the Department of Operations Research celebrated the 25th anniversary of its founding. The celebration was held in conjunction with an ORSA-TIMS Annual Meeting in San Francisco, so there was a big turnout of Stanford alumni. What everybody found remarkable is that, with the exception of Harvey Wagner and Rudy Kalman, essentially the entire core O.R. faculty from the 1960s onward was still there and going strong. However, changes would be coming soon.

1996-2021: Department of Management Science & Engineering becomes new home for operations researchBig organizational changes involving operations research began to take place at Stanford. The Department of Operations Research and Department of Engineering-Economic Systems merged in 1996, which was then merged with the Department of Industrial Engineering and Engineering Management in 2000 to form the Department of Management Science and Engineering (MS&E). Bringing together the distinguished faculties from the three heritage departments provided great strength in various areas of O.R. and its applications. (Although many members of the faculty in 2000 have since retired, such luminaries as Brandeau, Paté-Cornell and Glynn are still there.)

The MS&E department has added some outstanding faculty who at least overlap with O.R. and analytics.

• Yinyu Ye (a Ph.D. graduate of EES under George Dantzig’s supervision) specializes in mathematical optimization, including interior-point methods. He won the 2009 John von Neumann Theory Prize from INFORMS and the 2014 SIAM Optimization Prize.

• Jose Blanchet earned the 2010 INFORMS Erlang Prize, which is awarded to early career researchers for particularly outstanding contributions to applied probability.

• Ashish Goel and Amin Saberi are involved with algorithmic research.

• Nick Bambos is an expert on network architecture.

• Ramesh Johari deals with statistical and machine-learning techniques used by online platforms.

• Ben Van Roy is a leading expert on machine learning and reinforcement learning algorithms.

• Kay Giesecke studies stochastic financial models.

• Itai Ashlagi specializes in matching markets such as kidney exchange optimization.

• Additional O.R. talent is provided by three outstanding assistant professors – Markus Pelger, Aaron Sidford and Irene Lo.

• In addition, two Nobel Laureates in the Department of Economics mentioned earlier – Roth and Milgrom – also have courtesy appointments in MS&E.

Another feature of the department is that its research and courses remain at the cutting edge of the O.R. field, including current trends in analytics and data science.

The current generation of O.R. scholars in MS&E is fully carrying on the tradition of leadership in the field that epitomized the original O.R. department. The important additional strength of MS&E is that it surrounds the O.R. area with a unique focus on the interface of engineering, business and policy to provide a context for the O.R. research that befits a department in Silicon Valley. Research emphases include organizational behavior, entrepreneurship and technology innovation, technology ventures and the intersection of technology strategy and organizational learning. Thus, the O.R. research is conducted in the context of dealing with the real technological business world.

The formation of the MS&E department in 2000 represented something of the changing of the guard for the original Department of Operations Research

faculty. Lieberman had just passed away the preceding year and the great George Dantzig passed away in 2005 at the age of 90. To honor these giants of the field, I led fundraising drives in 2005 and 2006 (with good help from Pete Veinott and Dick Cottle) to establish an endowment fund that would provide multiple annual Dantzig-Lieberman Operations Research Fellowships in the MS&E department in perpetuity. The response was wonderful, including contributions from the hundreds of Ph.D. graduates of the Department of Operations Research. This endowment fund currently holds more than $4 million. Each year, Stanford sends me a packet of thank-you letters and resumes for the students who held one of these fellowships for at least part of that year. I am delighted that my department will continue to send top-notch young O.R. scholars into the field each year.

Lessons LearnedThe Stanford O.R. history suggests some lessons for other operations research programs.1. Stay on top of new trends. For example, recognize

that the term analytics (or data science or artificial intelligence) tends to convey much more to potential consumers of our field than the term operations research (or management science), so embrace these new terms.

2. Also embrace new advances in the field. For example, learn the valuable new techniques provided by analytics, data science and artificial intelligence and add them to your toolkit.

3. Be a missionary for this wonderful field. Perhaps add a course such as “The Role of Analytics in Society” for non-majors.

4. Stanford’s Department of Management Science and Engineering provides an interesting model for a core STEM department that combines our field and its complements.

5. It appears that the leadership of Stanford in O.R. and analytics will continue into future decades.

6. Enjoy the experience of being surrounded by exceptionally talented colleagues.

FREDERICK S. HILLIER is professor emeritus of operations research at Stanford University.

REFERENCE1. Hillier, F.S., 2021, “The Remarkable Ongoing Story of the Birth and

Longevity of the Classic Hillier-Lieberman Textbook,” June, OR/MS Today, https://doi.org/10.1287/orms.2021.03.04.

55

The current generation of O.R. scholars in MS&E is fully carrying on the tradition of leadership in the field that epitomized the original O.R. department.



MEMBER INSIGHTS

SEARCHING FOR THE CORRECT BACHELOR’S, master ’s, doctoral or certificate program can be like searching for a needle in a haystack. Many more program types, delivery methods and areas of study exist than just a few years ago.

If you Google “master’s in business analytics” you will get more than a million hits. This includes ads and some “best of ” listings, but how are these lists determined? Somewhere in all those listings is the program you are looking for – your perfect needle in this huge haystack – but how do you find it?

With the wide variety of online information available, finding the program that is right for you can be even more challenging. Reviews and rankings, though helpful, are based on what someone else determines is important.

INFORMS Academic Program DatabaseA student’s future success can be influenced by finding the right program. In response to this need, volunteers from several universities have worked with INFORMS to create a world-class database [1] with the goal to provide individuals with relevant information to help them make informed decisions about their studies.

INFORMS is dedicated to promoting best practices and advances in operations research, management science and analytics. As the field’s largest professional association, INFORMS and its members work to improve operational and decision-making processes and outcomes. As a nonprofit focused on advancing the science and technology of decision-making, a critical component is educating the future workforce in these areas. Therefore, INFORMS is uniquely positioned to provide an unbiased source of information about educational programs.

The INFORMS Academic Program Database is a free resource that students can use to narrow their search and find the best academic programs for their needs. Colleges and universities enter and maintain their information for free, ensuring all programs are on a level playing field to attract candidates that are a good fit for their respective programs.

With more than 100 different programs in the database currently, students can use seven program filters, each with many different categories, to narrow their search. When you find a program of interest, various information is provided including a direct point of contact, testing requirements, credit hours and estimated tuition.

Looking for a specific program in data science, machine learning, financial engineering or marketing analytics? These are just a few of the many programs you’ll find in the database. There are also more ways to narrow your search. If your requirements are more precise, such as a masters-level graduate school program in the southern United States classified as applied analytics, your search results would currently show five specific programs in the INFORMS Academic Program Database. Moreover, because universities maintain their own data, we expect the database to continue to grow with more programs and additional data over time. As the database continues to grow, INFORMS is working to expand to include international universities.

Visit the database to discover your pefect academic program at any level: www.informs.org/Resource- Center/INFORMS-Academic-Program-Database.

If you are an administrator or faculty at a university that has an analytics program and would like to be added to the INFORMS Academic Program Database, the process starts by becoming an administrator for your program. Visit https://education-admin.informs.org/users/register.php to register.

For any questions about the Academic Program Database, please contact Bill Griffin, INFORMS Professional Development Program Manager, at [email protected].

WENDY SWENSON-ROTH is clinical assistant professor of management at Georgia State University. She is an INFORMS member involved in many communities including the University Analytics Program Committee. She serves as vice chair of INFORMS Section on Finance.

REFERENCE1. INFORMS Academic Program Database, https://www.informs.org/

Resource-Center/INFORMS-Academic-Program-Database.

INFORMS ACADEMIC PROGRAM DATABASEBY WENDY SWENSON-ROTH

THE GROWING TRENDS OF DIGITAL DATA generation, low-cost computing resources and storage devices, and efficient, easy-to-use software have made analytics ubiquitous. As a result, analytics professionals are in high demand and analytics- focused courses and degree options are proliferating. The lengthy list of learning options can make the task of selecting the items most compatible with one’s personal interests and professional goals overwhelming; this problem of plenty paralyzes even current analytical professionals as they search for experiences to keep their skills updated.

Enter the INFORMS Academic Program Database [1], which markets itself as a “tool [that] allows you to search by dozens of different criteria so that you can narrow down your search quickly to find the program that is right for you.” It follows the pattern of other program search tools offered by organizations such as U.S. News & World Report, Peterson and the Princeton Review. What sets the INFORMS Academic Program Database apart from these is its free access, unhindered by advertisements and chat boxes, and easy navigation. An array of filters enables analytics professionals to quickly match their interests with the available courses and programs.

Navigating the DatabaseAn illustrated example will help highlight the features and functionality of the database, which enables users to find the results best suited to their needs.

Meet Jessica. Jessica is a Singapore native entering her second year of work as an analyst in one of Singapore’s leading investment banks, her first job since obtaining her undergraduate degree. While a valuable learning experience in itself, she realizes that her career would further benefit if she obtained a graduate education focused on data analytics. Her primary interest is in courses on formulating the right questions to derive business insights that can be leveraged to form cutting-edge strategies for her employer. She seeks a full-time program in the American South because her

brother is currently pursuing his doctoral degree in chemical engineering from the University of Florida. Four months ago, she took the GMAT and earned a good score.

Following are filters available in INFORMS Academic Program Database for search.

• Program Type: Enables you to customize the degree of your next learning venture. Options range from bachelor’s to doctoral. Jessica chose all of the M.S.- and MBA-related filters.

• Classification: Identifies the specific field (Course Tags) for the degree; for example, financial engineering for finance-based analytical programs. Jessica chose the “Business Analytics” filter.

• College Type: The “Business” type is best for students seeking to obtain business insights through analytics. “Arts & Sciences” would be a better fit for students interested in the methodological development aspects of Bayesian statistics. Jessica chose business colleges.

• Program Delivery Method: Extends the usefulness of the database for a wider range of analytical professionals. “On Campus: Full Time” covers traditional learning options whereas “Online Only: Part Time” extends options for working professionals. Jessica selected “On Campus Only: Full Time.”

• Testing Requirement: Often, standardized test results are required to make admission- related decisions. Test scores are valid over a couple of years, and this filter provides you the option to shortlist courses matching with valid test scores. If your GRE score is valid, you can shortlist all courses accepting GRE test scores. Jessica selected the “GMAT” filter.

• U.S. Region & U.S. State/Territory: Allows you to spatially narrow down your learning options in the United States, selecting from four regions and the 50 U.S. states plus American territories such as Puerto Rico. Jessica selected “South” in U.S. Region.

INFORMS ACADEMIC PROGRAM DATABASE: A TOOL TO IDENTIFY THE BEST ANALYTICS PROGRAMSBY MIHIR MEHTA AND ABIGAIL LINDNER

STUDENT PERSPECTIVES


STUDENT PERSPECTIVES

Applying these filters identified one result, the Master of Quantitative Management: Business Analytics Program from Duke University, as a potential option for Jessica (Figure 1).

Jessica also found information on the program website, including objectives, full-time duration, testing and other application requirements. She also learned about required credit hours and the financial aspects of the degree. She was very happy with the result and pleased that the database displayed the key information within the database itself, rather than redirecting her to another page. Moreover, the entry was updated in the current academic year cycle!

ObservationsJessica made a few key observations during the navigation process. The filter options are collapsed by default. Once expanded, the options stayed visible unless she manually collapsed the menu. Additionally, search results were dynamic in nature, automatically reflecting the effects of the last selection. “Clear Filters” let her restart the search when needed.

Megan, Jessica’s friend, found key information on doctoral programs in operations research (Figure 2). She read through the different school names and their delivery methods. By clicking on the results, she found more information on each program. The additional information had few minor variations.

Use CasesTo further understand the possible uses of the INFORMS Academic Program Database, use cases are provided from the perspectives of specific analytics enthusiasts, undergraduate students and experienced working professionals.

Analytics enthusiasts:• Program options for specific analytics

needs. Ryan, a Chicago native, has a bachelor’s degree in business. For the last

two years, he has worked as an associate consultant specialized in aspects of the supply chain. He would like to find analytical degrees incorporating his job skills, so he selects the “Supply Chain” option in the Classification Filter and finds engineering and management-driven options.

• Program options for generalized analytics needs. Tom, a budding analyst, is about to finish his undergraduate degree in information systems and wants to explore academic analytics programs providing an M.S. degree. He selects all four M.S. options in Program Type and selects the “Analytics” classification to gather information.

Undergraduate students: • Deciding analytics-driven elective courses.

Jill, an undergraduate student in business economics, used the database to look at analytical programs under “Management Science” to determine elective possibilities to add an analytical edge to her education.

Experienced working professionals: • Deciding professional growth strategies.

Cynthia, an experienced healthcare analyst, is experiencing a technological transition in her job on account of growing electronic health records (EHR) usage and migrations to distributed data processing approaches. She used the database to look at analytical programs by selecting “Information Systems” classification to devise learning strategies for her professional development.

Opportunities for Database ImprovementOne of the drawbacks that a user first engaging with the tool will discover is the small selection of schools. Given that most of the universities in the U.S. have multiple variations of analytics degree programs offering perspectives from business, engineering and science schools, the database covers about 100 programs. Furthermore, it is missing programs from outside the United States. This sparse program and university representation is partly explained by the way INFORMS collects the data, reporting that, “the data you see here is maintained by the universities themselves,” which eliminates advertisements and sponsored content [2]. Being an international society, INFORMS should make definite attempts to enhance program and university representation.

Other features worth re-evaluating are the filter options, which, while certainly concise, are slightly complicated. In Program Type, for instance, there are 15 options: four bachelor’s programs, six master’s programs, two Ph.D. programs, two certificate programs and one unspecified. Simplification to “Bachelor,” “Master,” “MBA,” “Certificate” and “Ph.D.” would be less overwhelming. The number of options for classification is appropriate given the diversity in

the analytics and OR/MS fields, but the developers of the tool might consider including, perhaps at the bottom of the page, an introductory glossary for the subject areas listed. This would especially benefit students and young professionals just beginning to deeply explore analytics.

Users should be aware that, due to the wealth of filter options and paucity of database entries, there are a handful of searches that yield no results. To name a few, selecting “Data Mining” classification, “PhD-Concentration” program type, “Online Only: Part Time” program delivery method, or “IELTS” testing requirement results in zero suggestions. In the U.S. State/Territory filter, Arizona, among other states, has no program representatives, despite Arizona State University boasting a Master of Science in Business Analytics program that in 2021 ranked in the top 10 for business analytics programs by U.S. News & World Report.

ConclusionPlaces for improvement notwithstanding, the INFORMS Academic Program Database as it exists now does offer a valuable resource for students and professionals exploring their interests in operations research and management science. The comprehensive annotations for the results give

prospective students an idea of what to anticipate in the programs at the universities that they will see in the database and from there contextualize their expectations for similar programs at unlisted universities. The database offers an accessible, clutter-free solution to the “problem of plenty” by presenting in the filter options the types of programs and range of academic focuses offered at universities across the United States. It has the capacity to assist all kinds of analytical professionals, from amateurs to experts, thanks to its variety of filter options. The database is rich in information and has the potential to immensely benefit students, academics and professionals in the INFORMS community.

MIHIR MEHTA is a doctoral student and research assistant in the Industrial and Manufacturing Engineering Department at Pennsylvania State University. He holds a master’s degree in statistics and has worked in the industry for over seven years in data science-driven roles. He serves as an editorial staff writer for OR/MS Tomorrow.

ABIGAIL LINDNER is an undergraduate student of mathematics who expects to finish her bachelor's degree at Regent University in fall 2021. She serves as an editorial staff writer for OR/MS Tomorrow.

REFERENCES1. https://www.informs.org/Resource-Center/INFORMS-Academic-

Program-Database2. Wendy Swenson-Roth, 2021, “INFORMS Academic Program Database,”

OR/MS Today, July 9, https://doi.org/10.1287/orms.2021.04.04.

FIGURE 2

FIGURE 1

CONGRATULATIONS TO THE FOLLOWING NEWLY-APPOINTED EDITORS-IN-CHIEF! Terms begin January 1, 2022 through December 31, 2024.

• Stefan Creemers, INFORMS Transactions on Education

• Olivier Toubia, Marketing Science

• Golbon Zakeri, INFORMS-Springer book series• Todd Zenger, Strategy Science


INFORMS JOURNALON OPTIMIZATION

Volume 1, Number 1Winter 2019

INFORMS JOURNALON DATA SCIENCE

Volume 1, Number 1August 2021

Volume 48, Number 1January–February 2018

INFORMS JOURNAL ON APPLIED ANALYTICSformerly INTERFACES

INFORMS IN ACTION

Solutions that fit one constituency may not fit all, but our journal editors have undertaken a number of innovations to improve time in review, including:

• clarifying reviewer instructions to achieve more focused feedback;

• establishing caps on the number of revisions a paper will receive before a final decision;

• creating expedited workflows for fast-tracking innovative research; and

• where appropriate, reorganizing editorial boards to better handle the editorial needs of the journal.

INFORMS is also more effectively utilizing the existing tools in the peer review software to manage due dates and proactively monitor for papers that are in danger of protracted review.

Time in ProductionSeveral years ago, it became clear that INFORMS was not equipped to handle the ongoing growth in accepted papers and their production for online and ultimately print publication. To provide long-term scalability, INFORMS completely restructured the production process, partnering with a publication services vendor to manage the life of a paper from acceptance through publication. From January 2019 forward, we began to see good progress in reducing time to publication, and to date have achieved a 39% reduction in production time.

Still, this reduction has not been as fast, sustained or significant as hoped. INFORMS does not view the status quo as being OK, and the Publication Department’s desire to fix its production time is as strong as ever.

Two factors are currently contributing to a stall in progress. First, our production vendor acquired their largest competitor in November 2020. Former rivals were charged with merging their workplace cultures, which, perhaps surprisingly, presented a much larger challenge than anticipated for aligning two workplaces that had been doing essentially the same type of work. In March 2021, INFORMS began to see a significant uptick in quality concerns, requiring several small publishing pauses while quality control measures were improved. Second, the tragic recent surge in COVID-19 in India has understandably resulted in a significant impact on our overseas production team. Whereas in February and March INFORMS published 200+ articles each month, we were only able to publish roughly 60 articles per month in April, May and June.

Still to ComeWhile significant efforts to restore operational excellence are top priority, there remain a number of improvements that we are excited to announce. Throughout 2020, INFORMS implemented several additional authentication pathways for ensuring that off-campus users could still access their institution’s subscription holdings while remote. This undoubtedly contributed to our journals

reaching just shy of 2.9 million downloads in 2020, a nearly 8% increase in usage over the previous year.

INFORMS also continues its efforts to improve the diversity, equity and inclusion (DEI) of the journals. In July 2021, Service Science published a comprehensive study of INFORMS’ editorial board diversity, which includes a network analysis of co-author relationships between editorial board members [1]. To improve transparency and accountability on this important issue, INFORMS has begun to publish dashboards of editorial board diversity on each journal website. Visit pubsonline.informs.org to check them out. In addition, this summer the INFORMS Board approved important changes to INFORMS governance policies to ensure that DEI is front and center of editorial search and review procedures, and INFORMS is working on developing anti-bias training for volunteer leaders to help recognize unconscious bias. INFORMS is grateful for the leadership and engagement of its volunteer community for helping to turn this vision into actions and policies.

Relatedly, further enhancements are underway for the PubsOnline platform to improve the accessibility of journal content, including the introduction of full-text XML for all INFORMS journals (this is currently in place for only three of them) and support for screen readers for users with visual impairment.

The Big PictureThe global community’s interest in INFORMS publications, as measured by submissions and downloads, is at an all-time high. So is the number of INFORMS journals and total pages produced. INFORMS understands that its ability to meet the challenges of refereeing and publishing this unprecedented volume of journal papers will in no small part determine our ability to remain at the forefront of disseminating the most significant operations research, management science and analytics studies in the coming decade. We are determined to continuously improve each of the interlocking systems that take our authors’ hard work from initial submission to widely disseminated INFORMS journal papers.

MATT WALLS ([email protected]) serves as INFORMS director of publications.

J. COLE SMITH ([email protected]), INFORMS vice president of publications, is dean of the College of Engineering & Computer Science, Syracuse University.

REFERENCES1. Laker J. Newhouse and Margaret L. Brandeau, 2021, "Who Are the

Gatekeepers? An Examination of Diversity in INFORMS Journal Editorial Boards," Service Science, July 9, https://doi.org/10.1287/serv.2021.0274.

CHALLENGES FACING INFORMS JOURNALS: THE WAY FORWARDBY MATT WALLS AND J. COLE SMITH

INFORMS JOURNALS PLAY A CRITICAL ROLE IN THE INSTITUTE’S strategic goal of advancing the science and technology of decision-making and elevating its impact. We rely on the creativity and tireless work of our community to write, referee and present their most impactful work via our 17 peer-reviewed journals.

The role of INFORMS in publishing this work is both a tremendous honor and a challenging endeavor. Our journals have seen consistent growth over the past decade – in 2020, INFORMS journals saw nearly 10,000 submissions, accepted more than 1,200 papers and published more than 20,000 pages across 83 issues. None of this would be possible without the support of the thousands of volunteers who serve as editors, editorial board members and reviewers, or the thousands of authors who entrust us to handle their submissions in a fair, ethical and professional manner.

INFORMS journals are well regarded, with seven appearing on the Financial Times FT50 list and many A-listed within business and engineering schools. However, a common point of feedback is that INFORMS is too slow in review and in getting accepted articles published online. With this article, we want to directly acknowledge this feedback and describe areas where we have made notable progress in reducing these delays. Where our efforts still need work – and several such areas indeed remain – we discuss some of the efforts that are underway to improve performance.

Time in ReviewEach INFORMS journal is reviewed every three years by a committee chaired by a prominent expert in that journal’s field. This review includes an in-depth stakeholder survey to ensure the journal is meeting the needs of its community. In addition, in 2020, INFORMS empaneled an ad hoc committee to comprehensively review the review process itself and provide recommendations for improvement.

Volume 29, Number 1March 2018

INFORMATION SYSTEMS RESEARCH

INFORMS JOURNALON COMPUTING


DECISION ANALYSIS


INFORMSTRANSACTIONS ON EDUCATION

Volume 28, Number 2Jannuary 2018

MANUFACTURING & SERVICE OPERATIONS MANAGEMENT


MANAGEMENT SCIENCE

Volume 64, Number 1January 2018

MARKETINGSCIENCE


MATHEMATICS OF OPERATIONS RESEARCH

Volume 43, Number 1 February 2018

OPERATIONS RESEARCH


ORGANIZATION SCIENCE


SERVICESCIENCE


STOCHASTIC SYSTEMS

Volume 7, Number 2December 2017

STRATEGYSCIENCE


TRANSPORTATIONSCIENCE


Visit pubsonline.informs.org to learn more and explore ground-breaking journal content!


INFORMS PRESIDENT-ELECT NOMINEES

to address our world’s greatest challenges – in academia, government and industry – excites me. I see opportunities to position OR/MS and analytics within the larger ecosystem of scientific disciplines identifying and tackling grand challenges. This would be a multifaceted effort that involves a focus on future research opportunities, partnerships with INFORMS’ sister societies and the National Academies, collaboration with industry and government agencies, strengthened DEI efforts, outreach to the public, and advocacy to government leaders.

Commitment to INFORMSIf elected, I would lead with passion and integrity to champion the profession, strengthen the membership, showcase our achievements and impact on society, and broadcast our message to the world. I look forward to building upon recent INFORMS successes, especially those involving the advocacy, membership and diversity initiatives, to take INFORMS and its members to new heights. I offer an outward and forward-looking vision for our discipline that can better position INFORMS members and the profession to have a seat at the table for new opportunities and tackling future challenges. I have the energy and vision to give back to the INFORMS community that has supported me throughout my career.

BioLaura A. Albert, Ph.D., is professor and department chair of industrial & systems engineering at the University of Wisconsin-Madison, and author of the blog “Punk Rock Operations Research.” Her research applies O.R. methodologies to address societally important problems and has advanced knowledge of how to efficiently and equitably allocate resources in public sector systems. She has earned the AAAS Fellow Award, Institute of Industrial and Systems Engineers Fellow Award, INFORMS Impact Prize, National Science Foundation CAREER award and Fulbright Award.

Laura has collaborated with practitioners from industry and government, and her research findings have been put in practice and influenced policy. She frequently engages the media to improve awareness of OR/MS and analytics and has advocated for its widespread use in government. As an educator, Laura is a tireless advocate for women and underrepresented groups. She has been involved with the Women in OR/MS Forum (WORMS) since she was a student, served on an ad hoc INFORMS DEI Committee, and helped pen the INFORMS Code of Conduct.

As a career-long INFORMS member, Laura has made many contributions to INFORMS and the OR/MS community. She served as president of WORMS and the Section on Public Sector OR. Notably, she served on the INFORMS Board as VP for marketing, communication and outreach, where she helped renew INFORMS online presence, reenergized INFORMS’ media efforts, led government advocacy efforts, and twice chaired the INFORMS Government & Analytics Summit.

LAURA A. ALBERTVision StatementI AM HONORED TO BE NOMINATED FOR the position of president-elect of INFORMS.

Opportunities for Innovation We are slowly emerging from the COVID-19 global pandemic. I see challenges but also opportunities for INFORMS and the OR/MS discipline. This past year we have seen so many of our members rise up to address our world’s greatest problems in public health, supply chains, equity in public sector systems and other areas. It is clear that many societally important problems remain that require OR/MS innovation, and it is my intention to position INFORMS and the profession to undertake these challenges. My previous experience, including four years on the INFORMS Board, leadership in INFORMS strategic planning, service in administrative roles at the University of Wisconsin-Madison, and a career-long commitment to diversity, equity and inclusion, have prepared me for these challenges.

Membership and MeetingsMembership is essential for ensuring the long-term financial health of INFORMS. In the coming years, it will be critical to enhance the membership experience, especially for practitioners. I am interested in exploring opportunities to broaden participation among students, practitioners, international members and the developing world. I look forward to ushering back in-person conferences while also expanding virtual and hybrid meetings that complement traditional conferences. Meetings provide an incentive for joining INFORMS while also stimulating innovation, enabling new collaborations, and inspiring students and the next generation.

A Sustained Commitment to DEIDiversity, equity and inclusion (DEI) require the same vigor and commitment we apply to achieving every other dimension of OR/MS excellence. I have been strongly committed to DEI over the course of my career and bring a wealth of experience to the table. It is important that INFORMS prioritizes equitable processes and policies that span education, strategic planning, programmatic considerations, community development and accountability. Together, INFORMS and the OR/MS profession can reach new heights when we create a community where everyone has opportunities and is valued.

Wicked Problems and the Ecosystem Surrounding OR/MSThis past year has made it abundantly clear that the OR/MS community can play a critical role in solving many important societal problems. I am proud to be part of a profession that has always stepped up to address our world’s “wicked” problems. Enabling INFORMS and its members

If elected, I would lead with passion and integrity to champion the profession, strengthen the membership, showcase our achievements and impact on society, and broadcast our message to the world.

MICHAEL P. JOHNSONVision StatementINFORMS IS THE LARGEST SOCIETY FOR the profession of operations research (O.R.) and analytics in the world. Our members represent universities, corporations, consultancies, government agencies and nonprofit organizations.

We are practitioners, scholars, researchers, administrators and entrepreneurs. We come from different countries and represent many diverse identities. What unites us is our commitment to data, analysis, evidence and a model-driven approach to decision-making to help our organizations operate more efficiently, provide products and services that improve people’s lives and ultimately enable our world to be a better place for all.

If elected president of INFORMS, I would aspire to ensure that our discipline and our profession can best meet the many different challenges our communities and societies face. These include healthcare, governance, supply chain resilience, racial inequality and climate change, among many others.

To meet these challenges, our profession must draw from the expertise and experiences of a diverse society, in which the membership of INFORMS more closely represents the makeup of the operations research and STEM professions. Recent data from the U.S. Census Bureau, Bureau of Labor Statistics and INFORMS indicate that women are underrepresented within INFORMS (28.5% of all members but 52.5% of the “O.R. analyst” profession), as are Blacks and Hispanics (2.6% and 6.9%, respectively, of all members but 7.0% and 13.2%, respectively, of the STEM profession).

This vital work will also require O.R./analytics expertise to be well-represented across all work sectors. While data and decision analytics are widely used in business and engineering, this is less so for local government and community- focused nonprofits. There is also less awareness of the promise of O.R./analytics among legislators, advocates and thought leaders than is the case for information technologies.

As president, I will work to enable the alignment of INFORMS activities with INFORMS’ values of integrity, impact, inclusion and innovation. First, I would like to develop and refine initiatives to enhance collaboration between INFORMS and other professional societies in STEM, public policy, planning and social sciences. Second, I would like to amplify the impact and influence of INFORMS on legislative, regulatory and policy issues, so we can set the terms of debate and innovations while championing our profession.

Third, I would like to increase INFORMS’ efforts to address the “leaky tech pipeline” that reduces opportunities in O.R./analytics for people from traditionally underrepresented groups. Next, I would like to ensure that the benefits of innovative INFORMS programs like Doing Good with Good O.R. and Pro Bono Analytics reach even more organizations that are in need of analytics support. Of course, I will support the work of our scholarly publications, communities and conferences that continue to produce world-class scholarship and practice innovations.

I’ve been a member of INFORMS since 1989 and I’ve served as president of two sections, on the board of directors as vice-president of chapters and fora, was founding chair of the Diversity, Equity and Inclusion committee and am a longtime member of Minority Issues Forum. My research on housing and community development has received funding from the National Science Foundation’s CAREER and Decision, Risk and Management Sciences programs as well as local foundations. I am chair of the Department of Public Policy and Public Affairs at University of Massachusetts Boston, an urban public research university. My academic training spans large public and private universities and a historically black college.

I believe that by embracing inclusive excellence and collaborating with professionals across disciplines and practice areas, we can have a transformative impact on our world. I welcome the opportunity to work on these tasks as president of INFORMS.

BioMichael P. Johnson is professor and chair of the Department of Public Policy and Public Affairs at the University of Massachusetts Boston. He received his Ph.D. in operations research from Northwestern University and B.S. in mathematics and French from Morehouse College. His research addresses decision modeling for nonprofit organizations and government agencies. His primary application areas include affordable and assisted housing, community development, climate change response, and diversity, equity and inclusion in the decision sciences. He is lead author of “Supporting Shrinkage: Planning and Decision-Making for Legacy Cities” (SUNY Press, 2021) and “Decision Science for Housing and Community Development: Localized and Evidence‐Based Responses to Distressed Housing and Blighted Communities” (Wiley, 2016). He has served as INFORMS vice president of chapters and fora and founding chair of the Diversity, Equity and Inclusion committee. His research has received support from the National Science Foundation, Abell Foundation and INFORMS DEI Ambassadors Program.

I believe that by embracing inclusive excellence and collaborating with professionals across disciplines and practice areas, we can have a transformative impact on our world.

2022 INFORMS BOARD OF DIRECTORS ELECTION (AUG. 2-SEPT. 30)For a full list of all open board positions and candidate profiles, as well as information on the 2022 election process, visit informs.org/About-INFORMS/Governance/INFORMS-Election.


CONFERENCE PREVIEW

ShanghaiTech UniversityProfessor/Associate Professor/

Assistant Professor

The School of Entrepreneurship and Management (SEM) at ShanghaiTech University now invites applications for Professor/Associate Professor/Assistant Professor in all fields of Operations Research, Management Science, Operations Management and Supply Chain Management. Applicants in Information System with expertise in database are also welcome to apply. Lateral moves to assistant professorship or untenured associate professorship will also be considered. The School of Entrepreneurship and Management at ShanghaiTech University is a new School with a unique mission to help innovators to become successful entrepreneurs and to train students with both management and technology skills. It is also building a world-class research center and a research-based think tank that combines theory with data analysis. Junior applicants should have (i) a PhD degree (when reporting duty); and (ii) high potential in teaching and research. Candidates for Associate and Full Professor posts are expected to have demonstrated academic leadership. Appointment with tenure can be offered to candidates with outstanding research and teaching record. Salary and benefits will be competitive, commensurate with experience and academic accomplishments.

Application Requirements: Cover Letter, CV, Job Market Paper, Teaching Evaluations, if applicable, Letters of Reference. Please submit all application materials electronically to [email protected] and [email protected]. For additional information of the SEM, please visit: sem.shanghaitech.edu.cn.

MASSACHUSETTS INSTITUTE OF TECHNOLOGY SLOAN SCHOOL OF MANAGEMENT

Faculty Positions in System Dynamics

The Massachusetts Institute of Technology (MIT) Sloan School of Management in Cambridge, MA, invites applications for tenure-track faculty positions in System Dynamics, to begin July 1, 2022, or as soon thereafter as possible. Strong candidates would: 1) know, or be committed to learn, classical System Dynamics methods and teach courses in the area; 2) and have a promising research program applying behavioral computational models to the management of organizational and social systems. System Dynamics at Sloan is closely affiliated with both management sciences and organization studies. Applicants keen on inter-disciplinary research connecting behaviorally grounded modeling with theory and practice in System Dynamics, Organizational Behavior, Strategy, Sociology, Psychology, Operations, Data Science, Public Policy, or Economics are particularly encouraged to apply. Duties will include research and teaching at the graduate and undergraduate levels.

Applicants should possess or be close to completion of a PhD in a relevant field as detailed above by the start date of employment. Applicants must submit: 1) an up-to-date curriculum vitae; 2) up to three representative publications; 3) a brief statement of objectives and aspirations in research and education; 4) an official graduate transcript; 5) information about teaching experience and performance evaluations (if available); and 6) three letters of recommendation by October 15, 2021.

Applications must be submitted via: https://apply.interfolio.com/90749

MIT is an equal opportunity employer committed to building a culturally diverse intellectual community, and strongly encourages applications from women and underrepresented minorities.

Check out INFORMS Analytics magazine, the leading online

publication for analytics professionals.

www.analytics-magazine.org

THE FLEXIBLE 2021 INFORMS ANNUAL Meeting, Oct. 24-27, (meetings.informs.org/ anaheim2021) will feature thousands of presentations showcasing the latest research and discoveries that share how operations research (O.R.) and analytics are saving lives, saving money and solving problems. With both in-person (in Anaheim, Calif.) and virtual attendance options, the content will be available to participants from anywhere around the globe.

The Annual Meeting general and program chairs are working closely with INFORMS Meetings Department to iron out the logistics of this hybrid event. INFORMS staff will be on-site in Anaheim to ensure a seamless event – no different than Annual Meetings from years past. This year, however, some INFORMS staff will stay grounded at headquarters in Catonsville, Md., managing important behind- the-scenes work and moderating virtual events.

On-site in AnaheimAnaheim is home to endless sun and soaring palm trees, and two of the most magical places on Earth – Disneyland and the first in-person INFORMS conference since the start of the COVID-19 pandemic. While INFORMS has enjoyed virtually connecting with members and meeting attendees, we truly can’t wait to welcome attendees face-to-face in October.

Details about the in-person event at the Anaheim Convention Center are still being ironed out – we all know the capriciousness of the COVID-19 virus and its variants – but the tried-and-true parts of the Annual Meeting will remain. Whether in-person or virtual, attendees will still experience the plethora of amazing content (including plenaries, keynotes and tutorials), and the networking events, Exhibit Hall and Career Fair will soldier on in hybrid fashion.

Virtual Attendees Will Still Experience the BestAs members decide whether to attend the meeting in Anaheim or from a virtual location, here is a reminder of the benefits both groups will have access to after registering for the 2021 Annual Meeting.

• Thousands of cutting-edge presentations, including daily plenary and keynote sessions, presented live to both in-person and virtual audiences, covering topics relating to the advancement of urban analytics and more.

• Specially selected sessions highlighting the diversity of the meeting tracks (presented in person and available via livestream for virtual attendees).

• TutORials in Operations Research series [1], an introduction to emerging and classical subfields of O.R. and management science, accessible to all members of the INFORMS community, regardless of career stage.

• Technology Tutorials sharing the latest software developments.

• Pre-meeting events including the Workshop on Data Mining & Decision Analytics [2] and INFORMS Combined Colloquia [3].

• Access to the full meeting program for three months following the conference.

Plenary HighlightsSeveral plenaries and keynotes are already scheduled for the 2021 Annual Meeting. Here is a sampling of exciting and thought-provoking topics:

• Companies such as Airbnb and Uber have fundamentally transformed society with their revolutionary business models. But with these novel changes come new challenges, the solutions to which Martin Savelsbergh, Georgia Tech, will explore in his plenary talk, “Challenges and Opportunities in Crowdsourced Delivery Planning and Operations.”

• In his session, “Roles of Optimization in Managing Amazon’s Supply Chain,” Huseyin Topaloglu takes attendees behind the scenes of Amazon’s inventory management and shares his personal experiences working with Amazon.

• The COVID-19 pandemic brought to light vulnerabilities in global supply chains, resulting in prolonged shortages of PPE, vaccines and more. Chris Tang, UCLA, will share his observations and discuss potential steps forward in his session, “Improving Supply Chain Resilience: Looking Back and Looking Forward.”

For the current list of plenaries and keynotes, visit http://meetings2.informs.org/wordpress/anaheim2021/plenaries-keynotes/.

INFORMS hopes you can join us in October in whichever way you are most comfortable. The most important thing is coming together to celebrate the incredible contributions and resiliency of the OR/MS community over the past year. As the event draws nearer, look for an updated conference preview from this year’s general chair, Julie Higle.

REFERENCES1. http://meetings2.informs.org/wordpress/anaheim2021/tutorials/2. http://meetings2.informs.org/wordpress/anaheim2021/

informs-workshop-on-data-mining-decision-analytics/3. http://meetings2.informs.org/wordpress/anaheim2021/

informs-combined-colloquia/

INFORMS’ FIRST FLEXIBLE CONFERENCE Cutting-edge content planned for virtual and in-person 2021 Annual Meeting.



LAST WORD

TEACHING OUR STUDENTS HOW TO FLY BY RICHARD C. LARSON

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Membership Magazinepubsonline.informs.org/magazine/orms-today/advertise

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Editor’s note: This article originally appeared in STEM Magazine. It has been slightly revised and is reprinted with permission.

“An investment in knowledge pays the best interest.” – Benjamin Franklin

SEEMS LIKE YESTERDAY, BUT IT WAS 1993 when my oldest son Erik was in middle school, in our hometown of Lexington, Mass. One night, as I was going to bed, I wished him a good night, as he was seated on the couch doing something on his color Apple Macintosh IIci. The next morning, I came downstairs and was surprised to see Erik where I left him, on the couch with the computer.

“Erik, you’re up early today!” “No Dad, I never went to sleep! Been here the

whole night!”“That’s crazy! What have you been doing?”“With two friends, I’m doing a project called

‘Mammals and Birds.’ Want to see what I did during the night?”

“I can’t wait!”Erik then proceeded to show me on the Mac

animated African birds (correctly drawn) flying through the forest and making native screeching sounds. I was stunned. Flabbergasted. Erik had never before stayed up late for school homework.

The idea of an overnighter on homework was inconceivable. And yet, it happened, and for his two email-connected collaborating friends as well. They were completing a computer-based project that I did not even know was possible with the technology of the day.

This event changed my life, as a faculty member at MIT who for many years focused on traditional teaching and research, to education as a process of active student engagement. What gets young people truly engaged in learning, especially STEM learning? (STEM = science, technology, engineering and math.) Our country depends on a growing pool of STEM-educated professionals to help catapult us in new discoveries in all aspects of society: medicine, environment, transportation, housing and much more. Members of INFORMS know this, seeing as INFORMS is a STEM-focused professional society.

As an MIT “lifer” who entered as a freshman a long time ago, I began reflecting on the way we taught most of our STEM courses: Standing chalk-and-talk lectures with students dutifully taking notes. The professors were content deliverers, and the students were content recipients.

In using blackboard pedagogy, MIT was no different from other universities, no different from most high schools and middle schools. This old-fashioned teaching model has been likened


LAST WORD

COMPARING THE IN-PERSON & VIRTUAL EXPERIENCES

Registration is open for the 2021 INFORMS Annual Meeting. This flexible conference allows you the option to choose the experience that works best for you, either in person or virtually.

MEETING ELEMENT IN PERSON VIRTUAL

Online Access to All Presentations October 24–27; Plus 3 Months After

Watch Plenaries, Keynotes, TutORials, & INFORMS Awards Ceremony in Real Time

In Conference Room via Live Stream

Ability to Watch Selected Sessions In Conference Room via Live Stream

Ability to Attend Technology Showcases via Monitor in Exhibit Hall or

Live Stream on Your Computer via Live Stream

Ability to Participate in Technical Sessions Online in Real Time with Live Q&A

Career Fair 11am–5pm Oct. 24 &

On-site Interviews Oct. 25–26 Online Prior to Conference

Exhibit Hall

Social/Networking Events In Conference Room Virtual Events

Ability to Run into Friends and Colleagues You Haven’t Seen in Person in 2 Years

REGISTER TODAY! Early Bird Deadline: September 27 meetings.informs.org/anaheim2021/registration

to the mass production process introduced by Henry Ford in 1908, building Model T Ford cars. All “works-in-progress” (cars or students) proceed at the same pace, with the eventual finished product being clones of those who have gone before. Mixing metaphors, the content-delivery model can be likened to Mother Bird shoving partially digested food down the throats of her chicks. Students, having digested the content, may spit it out the next week on a written test, and then forget it all soon thereafter.

How do you get students to become engaged in their own learning? Pull voluntary all-nighters to discover and “own” the science and math on the way to something bigger, more important than a grade on the next test or the score on a standardized test? Isn’t it most important to have Mother Bird teach their chicks how to fly?

Project-based Learning Gives Content WingsIn 1993, when Erik pulled that first all-nighter, I had not heard of “project-based learning” (PBL). It turns out that’s what he and his friends were doing – using STEM in creative, non-textbook ways to construct something new, something that they invent, they own. They were flying beautifully without Mother Bird!

Today, project-based learning is more mature, established and researched. Today’s PBL usually requires the students to collaboratively work in small teams for several weeks in and out of the classroom. Unfortunately, PBL is used in only a small amount of classrooms. The dominant teaching style remains the sitting-in-seats, passive-receiving, mass production model. Many students who could excel in STEM subjects become bored and internally “drop out.” They are not engaged; this is a huge loss for the students and for our country.

How do we get more PBL into high school STEM classes? Our 20 or so years of activity in this area, working with many STEM teachers, suggests that the biggest impediment to more use of PBL is lack of quality teacher training in PBL. Teachers need to feel comfortable and competent in designing and managing a multi-week PBL unit with small teams of students working together, in and out of the classroom.

During the weeks of the PBL exercise, all sorts of things can “go awry,” and teachers need to know how to manage these situations. Proper training, not traditional “professional development,” is needed for teachers to lead such a semistructured and complex learning environment, far from scripted lecturing.

In our work at MIT BLOSSOMS (https://blossoms.mit.edu), we have posted six PBL units with all the structural scaffolding a teacher would need to launch and manage a multi-week PBL exercise. These are described in a webinar: “Project-Based Learning, Allowing Every Student to Shine” [1].

For example, one of the PBL units starts with an interactive video on “flaws of averages,” and then suggests six different PBL units building from the video, each focusing on a safety issue in the students’ community.

One, for instance, has the PBL student team identify the 10 least safe traffic intersections in their community; this requires students to create a methodology based on physics, math and data indicating how they assess safety of an intersection. Students doing this project will have to discover and bring to their work many concepts of operations research, management science and analytics. For instance, a street intersection is a queueing system with randomness and only partial real-time information (due to imperfect sight lines). When finished, the students present their findings to a public audience. Such projects treat students as emerging adults, giving back to the community. What a wonderful way to let them discover some key concepts of OR/MS and analytics.

Education research has demonstrated substantial student excitement and engagement in multi-week PBL collaborative assignments. As a career-long proponent of OR/MS and analytics, I can't think of better community assignments than those that will require our type of problem formulation and thinking.

I know that many INFORMS members have spent myriad hours volunteering in secondary school STEM education. Let’s continue and expand that commitment, bringing PBL into the mix. Thousands of young people, to be fully engaged in their learning, will be grateful – as will our country. Let them fly high in the sky!

Author’s note: In 2002, Erik graduated from MIT, majoring in electrical engineering and computer science. He now is principal product manager for a company managing a talent marketplace for the skilled trades.

RICHARD C. LARSON ([email protected]) is Mitsui Professor, post-tenure, in the Institute for Data, Systems and Society of the Massachusetts Institute of Technology. He is founding director of MIT LINC (https://jwel.mit.edu/about-linc) and principal investigator of MIT BLOSSOMS (https://blossoms.mit.edu). Larson is a member of INFORMS and the U.S. National Academy of Engineering. He served as president of ORSA and INFORMS. His first book, “Urban Police Patrol Analysis” (MIT Press, 1972), was awarded the Lanchester Prize of ORSA. He has been honored with the INFORMS President’s Award and Kimball Medal. Larson served as co-director of the MIT Operations Research Center for more than 15 years. In 2017, he was given the first-ever Lifetime Achievement Daniel Berg Medal for “making significant contributions to technology innovation, service systems and strategic decision-making.”

REFERENCE1. Larson, R.C., 2020, “Project-Based Learning, Allowing Every

Student to Shine,” Future of Learning and Thought Leadership Webinar Series, Nov. 30, https://opendoor.col.org/webinar- project-based-learning-allowing-every-student-to-shine/.

Education research has demonstrated substantial student excitement and engagement in multi-week project-based learning collaborative assignments.

THE POWER OF AN INFORMS MEMBERSHIPWhy do successful business innovators, academic thought leaders, and students join INFORMS? As the largest international association for operations research and analytics professionals, with over 13,000 members, our community has the strength to empower its members with continuous learning and career growth, meaningful volunteering, and life-changing networking opportunities at globally recognized conferences. For the last 25 years, INFORMS members have come together to save lives, save money, and solve problems. Now let’s tackle the future together.

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UNDERGRAD SUPPLY OP-ED: ANTI-RACIST BUSINESS …