A worker carries a large bag of coffee beans on his shoulders

Top 40 Most Popular Case Studies of 2017

We generated a list of the 40 most popular Yale School of Management case studies in 2017 by combining data from our publishers, Google analytics, and other measures of interest and adoption. In compiling the list, we gave additional weight to usage outside Yale

We generated a list of the 40 most popular Yale School of Management case studies in 2017 by combining data from our publishers, Google analytics, and other measures of interest and adoption. In compiling the list, we gave additional weight to usage outside Yale.

Case topics represented on the list vary widely, but a number are drawn from the case team’s focus on healthcare, asset management, and sustainability. The cases also draw on Yale’s continued emphasis on corporate governance, ethics, and the role of business in state and society. Of note, nearly half of the most popular cases feature a woman as either the main protagonist or, in the case of raw cases where multiple characters take the place of a single protagonist, a major leader within the focal organization. While nearly a fourth of the cases were written in the past year, some of the most popular, including Cadbury and Design at Mayo, date from the early years of our program over a decade ago. Nearly two-thirds of the most popular cases were “raw” cases - Yale’s novel, web-based template which allows for a combination of text, documents, spreadsheets, and videos in a single case website.

Read on to learn more about the top 10 most popular cases followed by a complete list of the top 40 cases of 2017.  A selection of the top 40 cases are available for purchase through our online store . 

#1 - Coffee 2016

Faculty Supervision: Todd Cort

Coffee 2016 asks students to consider the coffee supply chain and generate ideas for what can be done to equalize returns across various stakeholders. The case draws a parallel between coffee and wine. Both beverages encourage connoisseurship, but only wine growers reap a premium for their efforts to ensure quality.  The case describes the history of coffee production across the world, the rise of the “third wave” of coffee consumption in the developed world, the efforts of the Illy Company to help coffee growers, and the differences between “fair” trade and direct trade. Faculty have found the case provides a wide canvas to discuss supply chain issues, examine marketing practices, and encourage creative solutions to business problems. 

#2 - AXA: Creating New Corporate Responsibility Metrics

Faculty Supervision: Todd Cort and David Bach

The case describes AXA’s corporate responsibility (CR) function. The company, a global leader in insurance and asset management, had distinguished itself in CR since formally establishing a CR unit in 2008. As the case opens, AXA’s CR unit is being moved from the marketing function to the strategy group occasioning a thorough review as to how CR should fit into AXA’s operations and strategy. Students are asked to identify CR issues of particular concern to the company, examine how addressing these issues would add value to the company, and then create metrics that would capture a business unit’s success or failure in addressing the concerns.

#3 - IBM Corporate Service Corps

Faculty Supervision: David Bach in cooperation with University of Ghana Business School and EGADE

The case considers IBM’s Corporate Service Corps (CSC), a program that had become the largest pro bono consulting program in the world. The case describes the program’s triple-benefit: leadership training to the brightest young IBMers, brand recognition for IBM in emerging markets, and community improvement in the areas served by IBM’s host organizations. As the program entered its second decade in 2016, students are asked to consider how the program can be improved. The case allows faculty to lead a discussion about training, marketing in emerging economies, and various ways of providing social benefit. The case highlights the synergies as well as trade-offs between pursuing these triple benefits.

#4 - Cadbury: An Ethical Company Struggles to Insure the Integrity of Its Supply Chain

Faculty Supervision: Ira Millstein

The case describes revelations that the production of cocoa in the Côte d’Ivoire involved child slave labor. These stories hit Cadbury especially hard. Cadbury's culture had been deeply rooted in the religious traditions of the company's founders, and the organization had paid close attention to the welfare of its workers and its sourcing practices. The US Congress was considering legislation that would allow chocolate grown on certified plantations to be labeled “slave labor free,” painting the rest of the industry in a bad light. Chocolate producers had asked for time to rectify the situation, but the extension they negotiated was running out. Students are asked whether Cadbury should join with the industry to lobby for more time?  What else could Cadbury do to ensure its supply chain was ethically managed?

#5 - 360 State Real Options

Faculty Supervision: Matthew Spiegel

In 2010 developer Bruce Becker (SOM ‘85) completed 360 State Street, a major new construction project in downtown New Haven. Just west of the apartment building, a 6,000-square-foot pocket of land from the original parcel remained undeveloped. Becker had a number of alternatives to consider in regards to the site. He also had no obligation to build. He could bide his time. But Becker worried about losing out on rents should he wait too long. Students are asked under what set of circumstances and at what time would it be most advantageous to proceed?

#6 - Design at Mayo

Faculty Supervision: Rodrigo Canales and William Drentell

The case describes how the Mayo Clinic, one of the most prominent hospitals in the world, engaged designers and built a research institute, the Center for Innovation (CFI), to study the processes of healthcare provision. The case documents the many incremental innovations the designers were able to implement and the way designers learned to interact with physicians and vice-versa.

In 2010 there were questions about how the CFI would achieve its stated aspiration of “transformational change” in the healthcare field. Students are asked what would a major change in health care delivery look like? How should the CFI's impact be measured? Were the center's structure and processes appropriate for transformational change? Faculty have found this a great case to discuss institutional obstacles to innovation, the importance of culture in organizational change efforts, and the differences in types of innovation.

This case is freely available to the public.

#7 - Ant Financial

Faculty Supervision: K. Sudhir in cooperation with Renmin University of China School of Business

In 2015, Ant Financial’s MYbank (an offshoot of Jack Ma’s Alibaba company) was looking to extend services to rural areas in China by providing small loans to farmers. Microloans have always been costly for financial institutions to offer to the unbanked (though important in development) but MYbank believed that fintech innovations such as using the internet to communicate with loan applicants and judge their credit worthiness would make the program sustainable. Students are asked whether MYbank could operate the program at scale? Would its big data and technical analysis provide an accurate measure of credit risk for loans to small customers? Could MYbank rely on its new credit-scoring system to reduce operating costs to make the program sustainable?

#8 - Business Leadership in South Africa’s 1994 Reforms

Faculty Supervision: Ian Shapiro

This case examines the role of business in South Africa's historic transition away from apartheid to popular sovereignty. The case provides a previously untold oral history of this key moment in world history, presenting extensive video interviews with business leaders who spearheaded behind-the-scenes negotiations between the African National Congress and the government. Faculty teaching the case have used the material to push students to consider business’s role in a divided society and ask: What factors led business leaders to act to push the country's future away from isolation toward a "high road" of participating in an increasingly globalized economy? What techniques and narratives did they use to keep the two sides talking and resolve the political impasse? And, if business leadership played an important role in the events in South Africa, could they take a similar role elsewhere?

#9 - Shake Shack IPO

Faculty Supervision: Jake Thomas and Geert Rouwenhorst

From an art project in a New York City park, Shake Shack developed a devoted fan base that greeted new Shake Shack locations with cheers and long lines. When Shake Shack went public on January 30, 2015, investors displayed a similar enthusiasm. Opening day investors bid up the $21 per share offering price by 118% to reach $45.90 at closing bell. By the end of May, investors were paying $92.86 per share. Students are asked if this price represented a realistic valuation of the enterprise and if not, what was Shake Shack truly worth? The case provides extensive information on Shake Shack’s marketing, competitors, operations and financials, allowing instructors to weave a wide variety of factors into a valuation of the company.

#10 - Searching for a Search Fund Structure

Faculty Supervision: AJ Wasserstein

This case considers how young entrepreneurs structure search funds to find businesses to take over. The case describes an MBA student who meets with a number of successful search fund entrepreneurs who have taken alternative routes to raising funds. The case considers the issues of partnering, soliciting funds vs. self-funding a search, and joining an incubator. The case provides a platform from which to discuss the pros and cons of various search fund structures.

40 Most Popular Case Studies of 2017

Click on the case title to learn more about the dilemma. A selection of our most popular cases are available for purchase via our online store .

  • Browse All Articles
  • Newsletter Sign-Up

Management →

management science case study

  • 12 Mar 2024
  • Research & Ideas

Publish or Perish: What the Research Says About Productivity in Academia

Universities tend to evaluate professors based on their research output, but does that measure reflect the realities of higher ed? A study of 4,300 professors by Kyle Myers, Karim Lakhani, and colleagues probes the time demands, risk appetite, and compensation of faculty.

management science case study

  • 29 Feb 2024

Beyond Goals: David Beckham's Playbook for Mobilizing Star Talent

Reach soccer's pinnacle. Become a global brand. Buy a team. Sign Lionel Messi. David Beckham makes success look as easy as his epic free kicks. But leveraging world-class talent takes discipline and deft decision-making, as case studies by Anita Elberse reveal. What could other businesses learn from his ascent?

management science case study

  • 16 Feb 2024

Is Your Workplace Biased Against Introverts?

Extroverts are more likely to express their passion outwardly, giving them a leg up when it comes to raises and promotions, according to research by Jon Jachimowicz. Introverts are just as motivated and excited about their work, but show it differently. How can managers challenge their assumptions?

management science case study

  • 05 Feb 2024

The Middle Manager of the Future: More Coaching, Less Commanding

Skilled middle managers foster collaboration, inspire employees, and link important functions at companies. An analysis of more than 35 million job postings by Letian Zhang paints a counterintuitive picture of today's midlevel manager. Could these roles provide an innovation edge?

management science case study

  • 24 Jan 2024

Why Boeing’s Problems with the 737 MAX Began More Than 25 Years Ago

Aggressive cost cutting and rocky leadership changes have eroded the culture at Boeing, a company once admired for its engineering rigor, says Bill George. What will it take to repair the reputational damage wrought by years of crises involving its 737 MAX?

management science case study

  • 16 Jan 2024
  • Cold Call Podcast

How SolarWinds Responded to the 2020 SUNBURST Cyberattack

In December of 2020, SolarWinds learned that they had fallen victim to hackers. Unknown actors had inserted malware called SUNBURST into a software update, potentially granting hackers access to thousands of its customers’ data, including government agencies across the globe and the US military. General Counsel Jason Bliss needed to orchestrate the company’s response without knowing how many of its 300,000 customers had been affected, or how severely. What’s more, the existing CEO was scheduled to step down and incoming CEO Sudhakar Ramakrishna had yet to come on board. Bliss needed to immediately communicate the company’s action plan with customers and the media. In this episode of Cold Call, Professor Frank Nagle discusses SolarWinds’ response to this supply chain attack in the case, “SolarWinds Confronts SUNBURST.”

management science case study

  • 02 Jan 2024
  • What Do You Think?

Do Boomerang CEOs Get a Bad Rap?

Several companies have brought back formerly successful CEOs in hopes of breathing new life into their organizations—with mixed results. But are we even measuring the boomerang CEOs' performance properly? asks James Heskett. Open for comment; 0 Comments.

management science case study

  • 12 Dec 2023

COVID Tested Global Supply Chains. Here’s How They’ve Adapted

A global supply chain reshuffling is underway as companies seek to diversify their distribution networks in response to pandemic-related shocks, says research by Laura Alfaro. What do these shifts mean for American businesses and buyers?

management science case study

  • 05 Dec 2023

What Founders Get Wrong about Sales and Marketing

Which sales candidate is a startup’s ideal first hire? What marketing channels are best to invest in? How aggressively should an executive team align sales with customer success? Senior Lecturer Mark Roberge discusses how early-stage founders, sales leaders, and marketing executives can address these challenges as they grow their ventures in the case, “Entrepreneurial Sales and Marketing Vignettes.”

management science case study

  • 31 Oct 2023

Checking Your Ethics: Would You Speak Up in These 3 Sticky Situations?

Would you complain about a client who verbally abuses their staff? Would you admit to cutting corners on your work? The answers aren't always clear, says David Fubini, who tackles tricky scenarios in a series of case studies and offers his advice from the field.

management science case study

  • 12 Sep 2023

Can Remote Surgeries Digitally Transform Operating Rooms?

Launched in 2016, Proximie was a platform that enabled clinicians, proctors, and medical device company personnel to be virtually present in operating rooms, where they would use mixed reality and digital audio and visual tools to communicate with, mentor, assist, and observe those performing medical procedures. The goal was to improve patient outcomes. The company had grown quickly, and its technology had been used in tens of thousands of procedures in more than 50 countries and 500 hospitals. It had raised close to $50 million in equity financing and was now entering strategic partnerships to broaden its reach. Nadine Hachach-Haram, founder and CEO of Proximie, aspired for Proximie to become a platform that powered every operating room in the world, but she had to carefully consider the company’s partnership and data strategies in order to scale. What approach would position the company best for the next stage of growth? Harvard Business School associate professor Ariel Stern discusses creating value in health care through a digital transformation of operating rooms in her case, “Proximie: Using XR Technology to Create Borderless Operating Rooms.”

management science case study

  • 28 Aug 2023

The Clock Is Ticking: 3 Ways to Manage Your Time Better

Life is short. Are you using your time wisely? Leslie Perlow, Arthur Brooks, and DJ DiDonna offer time management advice to help you work smarter and live happier.

management science case study

  • 15 Aug 2023

Ryan Serhant: How to Manage Your Time for Happiness

Real estate entrepreneur, television star, husband, and father Ryan Serhant is incredibly busy and successful. He starts his days at 4:00 am and often doesn’t end them until 11:00 pm. But, it wasn’t always like that. In 2020, just a few months after the US began to shut down in order to prevent the spread of the Covid-19 virus, Serhant had time to reflect on his career as a real estate broker in New York City, wondering if the period of selling real estate at record highs was over. He considered whether he should stay at his current real estate brokerage or launch his own brokerage during a pandemic? Each option had very different implications for his time and flexibility. Professor Ashley Whillans and her co-author Hawken Lord (MBA 2023) discuss Serhant’s time management techniques and consider the lessons we can all learn about making time our most valuable commodity in the case, “Ryan Serhant: Time Management for Repeatable Success.”

management science case study

  • 08 Aug 2023

The Rise of Employee Analytics: Productivity Dream or Micromanagement Nightmare?

"People analytics"—using employee data to make management decisions—could soon transform the workplace and hiring, but implementation will be critical, says Jeffrey Polzer. After all, do managers really need to know about employees' every keystroke?

management science case study

  • 01 Aug 2023

Can Business Transform Primary Health Care Across Africa?

mPharma, headquartered in Ghana, is trying to create the largest pan-African health care company. Their mission is to provide primary care and a reliable and fairly priced supply of drugs in the nine African countries where they operate. Co-founder and CEO Gregory Rockson needs to decide which component of strategy to prioritize in the next three years. His options include launching a telemedicine program, expanding his pharmacies across the continent, and creating a new payment program to cover the cost of common medications. Rockson cares deeply about health equity, but his venture capital-financed company also must be profitable. Which option should he focus on expanding? Harvard Business School Professor Regina Herzlinger and case protagonist Gregory Rockson discuss the important role business plays in improving health care in the case, “mPharma: Scaling Access to Affordable Primary Care in Africa.”

management science case study

  • 05 Jul 2023

How Unilever Is Preparing for the Future of Work

Launched in 2016, Unilever’s Future of Work initiative aimed to accelerate the speed of change throughout the organization and prepare its workforce for a digitalized and highly automated era. But despite its success over the last three years, the program still faces significant challenges in its implementation. How should Unilever, one of the world's largest consumer goods companies, best prepare and upscale its workforce for the future? How should Unilever adapt and accelerate the speed of change throughout the organization? Is it even possible to lead a systematic, agile workforce transformation across several geographies while accounting for local context? Harvard Business School professor and faculty co-chair of the Managing the Future of Work Project William Kerr and Patrick Hull, Unilever’s vice president of global learning and future of work, discuss how rapid advances in artificial intelligence, machine learning, and automation are changing the nature of work in the case, “Unilever's Response to the Future of Work.”

management science case study

How Are Middle Managers Falling Down Most Often on Employee Inclusion?

Companies are struggling to retain employees from underrepresented groups, many of whom don't feel heard in the workplace. What do managers need to do to build truly inclusive teams? asks James Heskett. Open for comment; 0 Comments.

management science case study

  • 20 Jun 2023

Elon Musk’s Twitter Takeover: Lessons in Strategic Change

In late October 2022, Elon Musk officially took Twitter private and became the company’s majority shareholder, finally ending a months-long acquisition saga. He appointed himself CEO and brought in his own team to clean house. Musk needed to take decisive steps to succeed against the major opposition to his leadership from both inside and outside the company. Twitter employees circulated an open letter protesting expected layoffs, advertising agencies advised their clients to pause spending on Twitter, and EU officials considered a broader Twitter ban. What short-term actions should Musk take to stabilize the situation, and how should he approach long-term strategy to turn around Twitter? Harvard Business School assistant professor Andy Wu and co-author Goran Calic, associate professor at McMaster University’s DeGroote School of Business, discuss Twitter as a microcosm for the future of media and information in their case, “Twitter Turnaround and Elon Musk.”

management science case study

  • 05 Jun 2023

Is the Anxious Achiever a Post-Pandemic Relic?

Achievement has been a salve for self-doubt for many generations. But many of the oldest members of Gen Z, who came of age amid COVID-19, think differently about the value of work. Will they forge a new leadership style? wonders James Heskett. Open for comment; 0 Comments.

management science case study

  • 25 Apr 2023

Using Design Thinking to Invent a Low-Cost Prosthesis for Land Mine Victims

Bhagwan Mahaveer Viklang Sahayata Samiti (BMVSS) is an Indian nonprofit famous for creating low-cost prosthetics, like the Jaipur Foot and the Stanford-Jaipur Knee. Known for its patient-centric culture and its focus on innovation, BMVSS has assisted more than one million people, including many land mine survivors. How can founder D.R. Mehta devise a strategy that will ensure the financial sustainability of BMVSS while sustaining its human impact well into the future? Harvard Business School Dean Srikant Datar discusses the importance of design thinking in ensuring a culture of innovation in his case, “BMVSS: Changing Lives, One Jaipur Limb at a Time.”

FOR EMPLOYERS

Top 10 real-world data science case studies.

Data Science Case Studies

Aditya Sharma

Aditya is a content writer with 5+ years of experience writing for various industries including Marketing, SaaS, B2B, IT, and Edtech among others. You can find him watching anime or playing games when he’s not writing.

Frequently Asked Questions

Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data scientists face when translating data into actionable insights in the corporate world.

Real-world data science projects come with common challenges. Data quality issues, including missing or inaccurate data, can hinder analysis. Domain expertise gaps may result in misinterpretation of results. Resource constraints might limit project scope or access to necessary tools and talent. Ethical considerations, like privacy and bias, demand careful handling.

Lastly, as data and business needs evolve, data science projects must adapt and stay relevant, posing an ongoing challenge.

Real-world data science case studies play a crucial role in helping companies make informed decisions. By analyzing their own data, businesses gain valuable insights into customer behavior, market trends, and operational efficiencies.

These insights empower data-driven strategies, aiding in more effective resource allocation, product development, and marketing efforts. Ultimately, case studies bridge the gap between data science and business decision-making, enhancing a company's ability to thrive in a competitive landscape.

Key takeaways from these case studies for organizations include the importance of cultivating a data-driven culture that values evidence-based decision-making. Investing in robust data infrastructure is essential to support data initiatives. Collaborating closely between data scientists and domain experts ensures that insights align with business goals.

Finally, continuous monitoring and refinement of data solutions are critical for maintaining relevance and effectiveness in a dynamic business environment. Embracing these principles can lead to tangible benefits and sustainable success in real-world data science endeavors.

Data science is a powerful driver of innovation and problem-solving across diverse industries. By harnessing data, organizations can uncover hidden patterns, automate repetitive tasks, optimize operations, and make informed decisions.

In healthcare, for example, data-driven diagnostics and treatment plans improve patient outcomes. In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve complex challenges in ways that were previously unimaginable.

What Is Management Science? + How to Enter This Field

Discover what management science encompasses and career options in this interdisciplinary field.

[Featured Image] A manager stands in front of a staff member at work on a laptop computer.

What is management science?

Management science is the study of problem-solving and decision-making in organizations. You can think of it as applying the scientific method to management, enabling managers to make decisions for an organization and improve its performance. For example, health care facilities can use management science to determine the necessary information systems, while airlines can use it to schedule planes and crew.

Management science is important because it helps organizations identify issues they must solve, streamline management efforts, use resources more effectively, and develop roadmaps for achieving goals. There are several assumptions or principles of management science that guide professionals in this field, including:  

Problem-solving is central to management. 

Managerial problems can be quantified and described in mathematical terms. 

Mathematical tools, techniques, simulations, and models can enhance the problem-solving process.

Originating from operations research , management science is interdisciplinary, which means it involves two or more academic or professional disciplines. Disciplines that management science intersects with include engineering, game theory, psychology, project management , data science , and supply chain management. 

Did you know? Operations research, management science’s predecessor, was developed during WWII when the Allied Forces (Great Britain, the United States, and the Soviet Union) used mathematical models to improve military operations.

In your research into management science, you may come across similar terms like scientific management or business analytics . Note their distinctions in the table below: 

Careers in management science

With a degree in management science, you can work in a variety of fields, from marketing and branding to finance, human resources, and data science. According to job site Zippia, the average median US salary for management science graduates is $68,844, with the top 10 percent earning over $120,000 [ 1 ]. The most popular job titles include:

Business analyst : Studies the market, determines a business’s profitability, and identifies solutions to a business’s challenges. 

Data analyst : Gathers and interprets data, highlights important trends, and reports findings to an organization’s management. 

Read more: Data Analyst vs. Business Analyst: What's the Difference?

Actuarial analyst: Works in the insurance industry and uses data analysis and statistical modeling to calculate the probability and risk of events like death, accidents, and property damage 

Finance analyst: Assesses the performance of stocks, bonds, and investments to advise businesses and individuals on their investment decisions

Programmer analyst: Tests, analyzes, and maintains software applications to help businesses achieve their goals 

Risk analyst: Analyzes financial documents and economic conditions to determine the risk involved in business decisions and planned activities. 

Research analyst: Collects data from varied sources to help organizations determine target markets and ideal pricing for products and services.

Your management science career path 

If you want to draw from multiple disciplines to solve business problems, take on a leadership role, and use analytical and critical thinking skills, management science could be an aligned career path for you. 

Follow the steps below to get started. 

1. Get a management science degree.

Getting a degree in management science or a related field, such as business statistics or international marketing, can expose you to the important concepts, methods, skills, and techniques for pursuing a rewarding career. Depending on the degree program, you can expect to take courses in statistics, financial accounting, systems analysis, data analysis, research design, statistics, marketing, operations research, decision risk analysis, and more. 

When you research management science degree programs, decide what criteria will make a program a good fit for you. You may find it useful to investigate programs’ rankings. For example, Universities.com ranks the management science programs at the University of Pennsylvania, Washington University in St. Louis, Cornell University, the University of Georgia, and the University of Southern California as the top five in the US. Rankings are based on such factors as

The diversity of the student body

Retention and graduation rates

Student-faculty ratio

Percentage of tenured or tenure-track faculty 

2. Hone your management science skills. 

In addition to earning your degree, you can benefit from continuing to build skills, knowledge, and industry insight in the broad field of management science. In continuing your education, you can better narrow down an area of focus or identify a specific career track for applying your management science training, such as data science or project management. 

Here are four approaches you can take: 

Become a member of the Institute for Operations Research and the Management Sciences (INFORMS) . 

Attend INFORMS conferences and events, take the organization’s professional development courses, and consider becoming a Certified Analytics Professional® .  

Attend conferences from other organizations listed on Conference Index . 

3. Build a management science resume. 

Once you decide on a focus area or specific career track, the next step is to build a resume that represents your capabilities. Be sure to list your education, certifications, specific skills, conferences you’ve attended, memberships in professional organizations, and other qualifications. 

Read more: 10 Ways to Enhance Your Resume

6. Apply for management science jobs. 

Start by researching current job openings on a variety of career sites, including general sites like Glassdoor, Indeed, or LinkedIn. Try search queries like “management science careers,” “management science jobs,” “management scientist,” or roles in your chosen area of focus.

For each job listing, pay close attention to salary information, qualifications required, and the tasks and responsibilities you’d be responsible for. Tailor your resume to each position. Practice interviewing skills like how to answer different types of questions and how to research a company. 

Learn management science with Coursera

Taking online courses can be a great way to gain some knowledge and skills in management science before or, at the same time, pursue a bachelor’s degree or advanced degree in this field.

If you’re ready to pursue an advanced degree, check out the Master of Science in Management program from the University of Illinois. 

Frequently Asked Questions (FAQ)

Is management science a good degree  ‎.

Management science can be a good degree to earn for several reasons. First, consider your interests and talents and how they might align with management science. Management science can be a rewarding career path for you if you want to work in a field that intersects with different disciplines, helps businesses perform better, and build leadership and critical thinking skills. 

Next, consider the versatility of a management science degree. Depending on your goals, you can apply it to a range of disciplines or industries, including marketing, game theory, and psychology. Lastly, the US Bureau of Labor Statistics (BLS) projects that jobs in operations research (an alternative term for management science) are expected to increase by 25 percent by 2030, which means there may be a variety of opportunities available to you as you advance your career 2 .  ‎

What can you do with a management science degree?  ‎

Management science is interdisciplinary and applies to various career paths that generally involve helping businesses improve performance and make scientifically informed decisions. Job titles you might come across in your job search include data analyst, business analyst, finance analyst, risk analyst, and more. Search job sites to discover opportunities that you may have with a management science degree.  ‎

Article sources

Zippia. “ Average Management Science Major Salary , https://www.zippia.com/management-science-major/salary/.” Accessed June 16, 2022. 

US Bureau of Labor Statistics. “ Operations Research Analysts , https://www.bls.gov/ooh/math/operations-research-analysts.htm.” Accessed June 16, 2022. 

Keep reading

Coursera staff.

Editorial Team

Coursera’s editorial team is comprised of highly experienced professional editors, writers, and fact...

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

  • Knowledge Base

Methodology

  • What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

  • Academic style
  • Vague sentences
  • Style consistency

See an example

management science case study

Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

  • Provide new or unexpected insights into the subject
  • Challenge or complicate existing assumptions and theories
  • Propose practical courses of action to resolve a problem
  • Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

  • Exemplify a theory by showing how it explains the case under investigation
  • Expand on a theory by uncovering new concepts and ideas that need to be incorporated
  • Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

Prevent plagiarism. Run a free check.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Normal distribution
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Ecological validity

Research bias

  • Rosenthal effect
  • Implicit bias
  • Cognitive bias
  • Selection bias
  • Negativity bias
  • Status quo bias

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 20). What Is a Case Study? | Definition, Examples & Methods. Scribbr. Retrieved March 20, 2024, from https://www.scribbr.com/methodology/case-study/

Is this article helpful?

Shona McCombes

Shona McCombes

Other students also liked, primary vs. secondary sources | difference & examples, what is a theoretical framework | guide to organizing, what is action research | definition & examples, unlimited academic ai-proofreading.

✔ Document error-free in 5minutes ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

  • Professional
  • International

Select a product below:

  • Connect Math Hosted by ALEKS
  • My Bookshelf (eBook Access)

Sign in to Shop:

Log In to My PreK-12 Platform

  • AP/Honors & Electives
  • my.mheducation.com
  • Open Learning Platform

Log In to My Higher Ed Platform

  • Connect Math Hosted by Aleks

Business and Economics

Accounting Business Communication Business Law Business Mathematics Business Statistics & Analytics Computer & Information Technology Decision Sciences & Operations Management Economics Finance Keyboarding Introduction to Business Insurance and Real Estate Management Information Systems Management Marketing Student Success

Humanities, Social Science and Language

American Government Anthropology Art Career Development Communication Criminal Justice Developmental English Education Film Composition Health and Human Performance

History Humanities Music Philosophy and Religion Psychology Sociology Student Success Theater World Languages

Science, Engineering and Math

Agriculture and Forestry Anatomy & Physiology Astronomy and Physical Science Biology - Majors Biology - Non-Majors Chemistry Cell/Molecular Biology and Genetics Earth & Environmental Science Ecology Engineering/Computer Science Engineering Technologies - Trade & Tech Health Professions Mathematics Microbiology Nutrition Physics Plants and Animals

Digital Products

Connect® Course management ,  reporting , and  student learning  tools backed by  great support .

McGraw Hill GO Greenlight learning with the new eBook+

ALEKS® Personalize learning and assessment

ALEKS® Placement, Preparation, and Learning Achieve accurate math placement

SIMnet Ignite mastery of MS Office and IT skills

McGraw Hill eBook & ReadAnywhere App Get learning that fits anytime, anywhere

Sharpen: Study App A reliable study app for students

Virtual Labs Flexible, realistic science simulations

Inclusive Access Reduce costs and increase success

LMS Integration Log in and sync up

Math Placement Achieve accurate math placement

Content Collections powered by Create® Curate and deliver your ideal content

Custom Courseware Solutions Teach your course your way

Professional Services Collaborate to optimize outcomes

Remote Proctoring Validate online exams even offsite

Institutional Solutions Increase engagement, lower costs, and improve access for your students

General Help & Support Info Customer Service & Tech Support contact information

Online Technical Support Center FAQs, articles, chat, email or phone support

Support At Every Step Instructor tools, training and resources for ALEKS , Connect & SIMnet

Instructor Sample Requests Get step by step instructions for requesting an evaluation, exam, or desk copy

Platform System Check System status in real time

Introduction to Management Science and Business Analytics: A Modeling and Case Studies Approach with Spreadsheets

Introduction to Management Science and Business Analytics: A Modeling and Case Studies Approach with Spreadsheets , 7th Edition

Format options:.

Lowest Price!

  • Print from $70.00
  • Connect from $154.66

McGraw Hill eBook

  • Highlight, take notes, and search
  • Download the free  ReadAnywhere app  for offline and mobile access

Watch to learn more about the eBook

Textbook Rental (150 Days Access)

  • Rent for a fraction of the printed textbook price
  • Complete text bound in hardcover or softcover

Loose-Leaf Purchase

Unbound loose-leaf version of full text

Shipping Options

  • Next-day air
  • 2nd-day air

Orders within the United States are shipped via FedEx or UPS Ground. For shipments to locations outside of the U.S., only standard shipping is available. All shipping options assume the product is available and that processing an order takes 24 to 48 hours prior to shipping.

Note:  Connect can only be used if assigned by your instructor. 

Connect (180 Days Access)

  • Digital access to a comprehensive online learning platform
  • Includes homework , study tools , eBook, and adaptive assignments
  • Download the free  ReadAnywhere app  to access the eBook offline

Connect + Loose-Leaf

  • Comprehensive online learning platform + unbound loose-leaf print text package
  • Connect includes homework , study tools, eBook, and adaptive assignments

* The estimated amount of time this product will be on the market is based on a number of factors, including faculty input to instructional design and the prior revision cycle and updates to academic research-which typically results in a revision cycle ranging from every two to four years for this product. Pricing subject to change at any time.

Instructor Information

Quick Actions ( Only for Validated Instructor Accounts ):

  • Table of Contents
  • Digital Platform
  • Author Bios
  • Accessibility

Affordability

Hiller's Management Science and Business Analytics: A Modeling and Case Studies Approach with Spreadsheets , 7e in its newest edition introduces both Management Science and Business Analytics in chapter 1 emphasizing the close relationship between them. The new Chapter 2 is an overview of how MS and BA professionals analyze problems and their similarities, chapter 3 is a new chapter on the role of data mining, clustering classification prediction methodology, some algorithms for implementing this methodology, a powerful SW package for data mining and more.  Chapter 4 will continue to focus on key techniques of Business Analytics to complete part 1 of the text and the final chapters are focused on Prescriptive and Predictive Analytics based on models based on Linear programming models, Other Certainty Models, and Uncertainty Models.

Main Features

  • LMS Integration
  • Print/Loose-Leaf Book Add-On Availability
  • Presentation Slides & Instructor Resources
  • Question & Test Banks
  • Adaptive Assignments
  • Student Progress Reporting & Analytics
  • Essay Prompts
  • Prebuilt Courses
  • Interactive Exercises
  • eBook Access (ReadAnywhere App)
  • Remote Proctoring (Proctorio)
  • Subject-Specific Tools

About the Author

Frederick Hillier

Professor emeritus of operations research at Stanford University. Dr. Hillier is especially known for his classic, award-winning text, Introduction to Operations Research, co-authored with the late Gerald J. Lieberman, which has been translated into well over a dozen languages and is currently in its 8th edition. The 6th edition won honorable mention for the 1995 Lanchester Prize (best English-language publication of any kind in the field) and Dr. Hillier also was awarded the 2004 INFORMS Expository Writing Award for the 8th edition. His other books include The Evaluation of Risky Interrelated Investments, Queueing Tables and Graphs, Introduction to Stochastic Models in Operations Research, and Introduction to Mathematical Programming. He received his BS in industrial engineering and doctorate specializing in operations research and management science from Stanford University. The winner of many awards in high school and college for writing, mathematics, debate, and music, he ranked first in his undergraduate engineering class and was awarded three national fellowships (National Science Foundation, Tau Beta Pi, and Danforth) for graduate study. Dr. Hillier’s research has extended into a variety of areas, including integer programming, queueing theory and its application, statistical quality control, and production and operations management. He also has won a major prize for research in capital budgeting.

Mark Hillier

Associate professor of quantitative methods at the School of Business at the University of Washington. Dr. Hillier received his BS in engineering (plus a concentration in computer science) from Swarthmore College, and he received his MS with distinction in operations research and PhD in industrial engineering and engineering management from Stanford University. As an undergraduate, he won the McCabe Award for ranking first in his engineering class, won election to Phi Beta Kappa based on his work in mathematics, set school records on the men’s swim team, and was awarded two national fellowships (National Science Foundation and Tau Beta Pi) for graduate study. During that time, he also developed a comprehensive software tutorial package, OR Courseware, for the Hillier-Lieberman textbook, Introduction to Operations Research. As a graduate student, he taught a PhD-level seminar in operations management at Stanford and won a national prize for work based on his PhD dissertation. At the University of Washington, he currently teaches courses in management science and spreadsheet modeling.

Creating accessible products is a priority for McGraw Hill. We make accessibility and adhering to WCAG AA guidelines a part of our day-to-day development efforts and product roadmaps.

For more information, visit our  accessibility page , or contact us at  [email protected]

Reduce course material costs for your students while still providing full access to everything they need to be successful. It isn't too good to be true - it's Inclusive Access.

Need support?    We're here to help -  Get real-world support and resources every step of the way.

Company Info

  • Contact & Locations
  • Diversity, Equity & Inclusion
  • Social Responsibility
  • Investor Relations
  • Social Media Directory
  • Place an Order
  • Get Support
  • Contact Customer Service
  • Contact Sales Rep
  • Check System Status

Additional Resources

  • Permissions
  • Author Support
  • International Rights
  • Purchase Order

Follow McGraw Hill:

©2024 McGraw Hill. All Rights Reserved.

AICPA SOC Logo

A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Never just tech

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

  • “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
  • “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
  • “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

  • data scientist:
  • prompt engineering
  • in-context learning
  • bias detection
  • pattern identification
  • reinforcement learning from human feedback
  • hyperparameter/large language model fine-tuning; transfer learning
  • data engineer:
  • data wrangling and data warehousing
  • data pipeline construction
  • multimodal processing
  • vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

  • Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
  • Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
  • Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

  • Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
  • Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
  • Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

Eric Lamarre

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

Explore a career with us

Related articles.

Light dots and lines evolve into a pattern of a human face and continue to stream off the the side in a moving grid pattern.

The economic potential of generative AI: The next productivity frontier

A yellow wire shaped into a butterfly

Rewired to outcompete

A digital construction of a human face consisting of blocks

Meet Lilli, our generative AI tool that’s a researcher, a time saver, and an inspiration

Help | Advanced Search

Electrical Engineering and Systems Science > Systems and Control

Title: quantifying the aggregate flexibility of ev charging stations for dependable congestion management products: a dutch case study.

Abstract: Electric vehicles (EVs) play a crucial role in the transition towards sustainable modes of transportation and thus are critical to the energy transition. As their number grows, managing the aggregate power of EV charging is crucial to maintain grid stability and mitigate congestion. This study analyses more than 500 thousand real charging transactions in the Netherlands to explore the challenge and opportunity for the energy system presented by EV growth and smart charging flexibility. Specifically, it analyses the collective ability to provide congestion management services according to the specifications of those services in the Netherlands. In this study, a data-driven model of charging behaviour is created to explore the implications of delivering dependable congestion management services at various aggregation levels and types of service. The probability of offering specific grid services by different categories of charging stations (CS) is analysed. These probabilities can help EV aggregators, such as charging point operators, make informed decisions about offering congestion mitigation products per relevant regulations and distribution system operators to assess their potential. The ability to offer different flexibility products, namely re-dispatch and capacity limitation, for congestion management, is assessed using various dispatch strategies. Next, machine learning models are used to predict the probability of CSs being able to deliver these products, accounting for uncertainties. Results indicate that residential charging locations have significant potential to provide both products during evening peak hours. While shared EVs offer better certainty regarding arrival and departure times, their small fleet size currently restricts their ability to meet the minimum order size of flexible products.

Submission history

Access paper:.

  • Download PDF
  • HTML (experimental)
  • Other Formats

license icon

References & Citations

  • Google Scholar
  • Semantic Scholar

BibTeX formatted citation

BibSonomy logo

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

  • Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

management science case study

A fuzzy interval dynamic optimization model for surface and groundwater resources allocation under water shortage conditions, the case of West Azerbaijan Province, Iran

  • Research Article
  • Published: 18 March 2024

Cite this article

  • Prshang Azari 1 ,
  • Soheil Sobhanardakani   ORCID: orcid.org/0000-0002-6038-0514 2 ,
  • Mehrdad Cheraghi 2 ,
  • Bahareh Lorestani 2 &
  • Amirreza Goodarzi 3  

70 Accesses

Explore all metrics

The allocation of water in areas which face shortage of water especially during hot dry seasons is of utmost importance. This is normally affected by various factors, the management of which takes a lot of time and energy with efforts falling infertile in many cases. In recent years, scholars have been trying to investigate the applicability of fuzzy interval optimization models in attempts to address the problem. However, a review of literature indicates that in applicating such models, the dynamic nature of the problem has mostly been overlooked. Therefore, the aim of the present study is to provide a fuzzy interval dynamic optimization model for the allocation of surface and groundwater resources under water shortage conditions in West Azerbaijan Province, Iran. In so doing, an optimization model for the allocation of water resources was designed and then was validated by removing surface and groundwater resources and analyzing its performance once these resources were removed. The model was then applied in the case study of ten regions in West Azerbaijan Province and the optimal allocation values and water supply percentages were determined for each region over 12 periods. The results showed that the increase in total demand has the greatest effect while the increase in groundwater industrial demand has the least effect on the supply reduction rate. The increase of uncertainty up to 50% in the fuzzy interval programming would lead to subsequent increases in groundwater extraction by up to 19% and decreases in water supply by up to 10%. The increase of uncertainty in the fuzzy interval dynamic model would cause an increase in groundwater extraction to slightly more than 10% and a decrease in water supply to 0.05%. Therefore, implementing the fuzzy interval dynamic programming model would result in better gains and would reduce uncertainty effects. This would imply that using a mathematical model can result in better gains and can provide better footings for more informed decisions by authorities for managing water resources.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

management science case study

Data availability

The authors declare that they did not need research data support with this submission. Also, the authors are sure that all data and materials as well as software application or custom code support their published claims and comply with field standards.

Alfaisal FM, Alam S, Alharbi RS, Kaur K, Khan MA, Athar MF, Rahin SA (2023) Application of an optimization model for water supply chain using storage reservoir operation for efficient irrigation system. Discrete Dyn Nat Soc 2023:1–13

Article   Google Scholar  

Alizamir M, Sobhanardakani S (2018) An artificial neural network - particle swarm optimization (ANN- PSO) approach to predict heavy metals contamination in groundwater resources. Jundishapur J Health Sci 10(2):e67544

Aslan Ö, Altan A, Hacıoğlu R (2022) Level control of blast furnace gas cleaning tank system with fuzzy based gain regulation for model reference adaptive controller. Processes 10(12):2503

Article   CAS   Google Scholar  

Berbel J, Exposito A (2022) A decision model for stochastic optimization of seasonal irrigation-water allocation. Agr Water Manage 262:107419

Calvete HI, Galé C, Iranzo JA, Mateo PM (2023) A decision tool based on bilevel optimization for the allocation of water resources in a hierarchical system. Int Trans Oper Res 30(4):1673–1702

Article   MathSciNet   Google Scholar  

Elleuch MA, Anane M, Euchi J, Frikha A (2019) Hybrid fuzzy multi-criteria decision making to solve the irrigation water allocation problem in the Tunisian case. Agr Sys 176:102644

Fan X, Zhang W, Chen W, Chen B (2020) Land–water–energy nexus in agricultural management for greenhouse gas mitigation. Appl Energy 265:114796

Ghobadi A, Cheraghi M, Sobhanardakani S, Lorestani B, Merrikhpour H (2020) Hydrogeochemical characteristics, temporal and spatial variations for evaluation of groundwater quality of Hamedan-Bahar Plain as a major agricultural region, west of Iran. Environ Earth Sci 79:428

Article   ADS   CAS   Google Scholar  

Ghobadi A, Cheraghi M, Sobhanardakani S, Lorestani B, Merrikhpour H (2022) Groundwater quality modeling using a novel hybrid data-intelligence model based on grey wolf optimization algorithm and multi-layer perceptron artificial neural network, case study: Asadabad plain, Hamedan Iran. Environ Sci Pollut Res 29(6):8716–8730

Hao N, Sun P, He W, Yang L, Qiu Y, Chen Y, Zhao W (2022) Water resources allocation in the Tingjiang River Basin: construction of an interval-fuzzy two-stage chance-constraints model and its assessment through Pearson correlation. Water 14(18):2928

Huang Z, Liu X, Sun S, Tang Y, Yuan X, Tang Q (2021) Global assessment of future sectoral water scarcity under adaptive inner-basin water allocation measures. Sci Total Environ 783:146973

Article   ADS   CAS   PubMed   Google Scholar  

Jia W, Wei Z, Zhang L (2022) A novel prediction and planning model for the benefit of irrigation water allocation based on deep learning and uncertain programming. Water 14(5):689

Kang A, Li J, Lei X, Ye M (2020) Optimal allocation of water resources considering water quality and the absorbing pollution capacity of water. Water Res 47:336–347

Karasu S, Altan A, Bekiros S, Ahmad W (2020) A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series. Energy 212:118750

Laszczyk M, Myszkowski PB (2019) Improved selection in evolutionary multi-objective optimization of multi-skill resource-constrained project scheduling problem. Inform Sci 481:412–431

Li F, Zhang G, Hu S (2022) An algorithm for optimal allocation of water resources in receiving areas based on adaptive decreasing inertia weights. J Adv Transport 2022:3329628

Google Scholar  

Li Y, Han Y, Liu B, Li H, Du X, Wang Q, Wang X, Zhu X (2023) Construction and application of a refined model for the optimal allocation of water resources—taking Guantao County China as an Example. Ecol Indic 146:109929

Liu WQ, Sun YG, Gu SH, He JK (2022) Game analysis for conflicts in water resource allocation. Syst Eng-Theory Pract 1:16–25

Martinsen G, Liu S, Mo X, Bauer-Gottwein P (2019) Joint optimization of water allocation and water quality management in Haihe River basin. Sci Total Environ 654:72–84

Naghdi S, Bozorg-Haddad O, Khorsandi M, Chu X (2021) Multi-objective optimization for allocation of surface water and groundwater resources. Sci Total Environ 776:146026

Nel JB, Mativenga PT, Marnewick AL (2022) A framework to support the selection of an appropriate water allocation planning and decision support scheme. Water 14(12):1854

Ramírez RM, Juárez MLA, Mora RD, Morales LDP, Mariles ÓAF, Reséndiz AM, Elizondo EC, Paredes RBC (2021) Operation policies through dynamic programming and genetic algorithms, for a reservoir with irrigation and water supply uses. Water Res Manage 35(5):1573–1586

Ren J, Khayatnezhad M (2021) Evaluating the stormwater management model to improve urban water allocation system in drought conditions. Water Suppl 21(4):1514–1524

Sabale R, Venkatesh B, Jose M (2023) Sustainable water resource management through conjunctive use of groundwater and surface water: a review. Innov Infrastruct Solut 8(1):17

Srinivas N, Deb K (1995) Multiobjective optimization using nondominated sorting in genetic algorithms. J Evol Comput 2(3):221–248

Sun J, Li YP, Suo C, Liu J (2020) Development of an uncertain water-food-energy nexus model for pursuing sustainable agricultural and electric productions. Agr Water Manage 241:106–384

Suo M, Xia F, Fan Y (2022) A fuzzy-interval dynamic optimization model for regional water resources allocation under uncertainty. Sustainability 14(3):1096

Taye MM (2023) Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers 12(5):91

Wang W, Jing R, Zhao Y, Zhang C, Wang X (2020) A load-complementarity combined flexible clustering approach for large-scale urban energy-water nexus optimization. Appl Energy 270:115163

Yang GQ, Li M, Guo P (2022) Monte Carlo-based agricultural water management under uncertainty: a case study of Shijin Irrigation District China. J Environ Informat 39(2):152–164

Yusoff PY, Ngadiman MS, Zain AM (2011) Overview of NSGA-II for optimizing machining process parameters. Procedia Eng 15:3978–3983

Download references

Acknowledgements

The authors are grateful to all supporting organizations for providing facilities to conduct and complete this study.

Author information

Authors and affiliations.

Department of Environmental Engineering, College of Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran

Prshang Azari

Department of the Environment, College of Basic Sciences, Hamedan Branch, Islamic Azad University, Hamedan, Iran

Soheil Sobhanardakani, Mehrdad Cheraghi & Bahareh Lorestani

Department of Civil Engineering, College of Engineering, Hamedan Branch, Islamic Azad University, Hamedan, Iran

Amirreza Goodarzi

You can also search for this author in PubMed   Google Scholar

Contributions

Material preparation, data collection, and analysis were performed by Prshang Azari, Soheil Sobhanardakani, Mehrdad Cheraghi, Bahareh Lorestani, and Amirreza Goodarzi. The first draft of the manuscript was written by Prshang Azari and Soheil Sobhanardakani, and all authors commented on previous versions of the manuscript. The corresponding author ensured that all the listed authors have approved the manuscript before submission, including the metadata.

Corresponding author

Correspondence to Soheil Sobhanardakani .

Ethics declarations

Ethics approval and consent to participate.

This article does not contain any studies with animals and human subjects. The authors confirm that all the research meets ethical guidelines and adheres to the legal requirements of the study country.

Consent for publication

The authors declare that this manuscript does not contain any individual person’s data and material in any form.

Conflict of interest

The authors declare no competing interests.

Additional information

Responsible Editor: Marcus Schulz

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Azari, P., Sobhanardakani, S., Cheraghi, M. et al. A fuzzy interval dynamic optimization model for surface and groundwater resources allocation under water shortage conditions, the case of West Azerbaijan Province, Iran. Environ Sci Pollut Res (2024). https://doi.org/10.1007/s11356-024-32919-5

Download citation

Received : 07 December 2023

Accepted : 11 March 2024

Published : 18 March 2024

DOI : https://doi.org/10.1007/s11356-024-32919-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Water resources allocation
  • Multi-objective optimization
  • Fuzzy interval programming
  • Dynamic programming
  • Find a journal
  • Publish with us
  • Track your research

IMAGES

  1. How to Write a Business Case Study: Tips, Steps, Mistakes

    management science case study

  2. 😎 Scientific management case study. Scientific management ryan air case

    management science case study

  3. (PDF) The Case for Case Studies in Management Research

    management science case study

  4. (PDF) Case Studies and Research in Management Science

    management science case study

  5. Introduction to Management Science

    management science case study

  6. What is a Business Case Study and How to Write with Examples

    management science case study

VIDEO

  1. Insights into Project Management_Alumni Lecture Series

  2. A session on Case studies to explain Innovation Ideas

  3. Sung Valley REE Mining

  4. Case Study Presentation Group 9

  5. MANAGEMENT CONCEPTS&PRINCIPLES I MANAGEMENT CONCEPTS I PART 2

  6. Webinar on “How to write an impactful Case Study” With Prof. Michael Goldman

COMMENTS

  1. Top 40 Most Popular Case Studies of 2021

    Fifty four percent of raw case users came from outside the U.S.. The Yale School of Management (SOM) case study directory pages received over 160K page views from 177 countries with approximately a third originating in India followed by the U.S. and the Philippines. Twenty-six of the cases in the list are raw cases.

  2. Top 40 Most Popular Case Studies of 2017

    Case Study Research & Development (CRDT) | December 19, 2017. We generated a list of the 40 most popular Yale School of Management case studies in 2017 by combining data from our publishers, Google analytics, and other measures of interest and adoption. In compiling the list, we gave additional weight to usage outside Yale. We generated a list ...

  3. Management Articles, Research, & Case Studies

    Professor Ashley Whillans and her co-author Hawken Lord (MBA 2023) discuss Serhant's time management techniques and consider the lessons we can all learn about making time our most valuable commodity in the case, "Ryan Serhant: Time Management for Repeatable Success.". 08 Aug 2023. Research & Ideas.

  4. PDF Management: Theory and Practice, and Cases

    the case method as developed and used at the Harvard Business School are not without controversy. The controversy is especially apparent in major research universities with business schools. I studied management at a research university whereby the traditional arts and sciences faculties exerted strong influence and control in the university and

  5. (PDF) Case Studies and Research in Management Science

    The case study method is an important pedagogical tool in the field of management science (Abdessemed, 2005). The production of "pedagogical case studies" for

  6. Introduction to Management Science: A Modeling and Case Studies

    The Sixth edition of Introduction to Management Science focuses on business situations, including prominent non-mathematical issues, the use spreadsheets, and involves model formulation and assessment more than model structuring. The text has three key elements: modeling, case studies, and spreadsheets.In addition to examples, nearly every chapter includes one or two case studies patterned ...

  7. Introduction to Management Science: A Modeling and Case Studies

    Introduction to Management Science, 5e, offers a unique model approach and integrates the use of Excel. Through this approach students are better able to grasp the essential concepts covered in the course and see their utility. ... Each chapter includes a case study that is meant to show the students a real and interesting application of the ...

  8. Applications of Operations Research and Management Science: Case

    This book includes case studies that examine the application of operations research to improve or increase efficiency in industry and operational activities. This collection of "living case studies" is all based on the author's 30-year career of consulting and advisory work. These true-to life industrial applications illustrate the ...

  9. Introduction to Management Science: A Modeling and Case Studies

    Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets, 5/e. Frederick S. Hillier, Stanford University Mark S. Hillier, University of Washington. To learn more about the book this website supports, please visit its Information Center ...

  10. Current Issue

    The Institute for Operations Research and the Management Sciences. 5521 Research Park Drive, Suite 200 Catonsville, MD 21228 USA. phone 1 443-757-3500. phone 2 800-4INFORMS (800-446-3676) fax 443-757-3515. email [email protected] Get the Latest Updates Discover INFORMS; Explore OR & Analytics; Get Involved;

  11. Introduction to Management Science: A Modeling and Case Studies

    Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets Frederick S. Hillier , Mark S. Hillier McGraw-Hill/Irwin , 2007 - Business & Economics - 602 pages

  12. A Review of Case Study Method in Operations Management Research

    This article reviews the case study research in the operations management field. In this regard, the paper's key objective is to represent a general framework to design, develop, and conduct case study research for a future operations management research by critically reviewing relevant literature and offering insights into the use of case method in particular settings.

  13. 10 Real-World Data Science Case Studies Worth Reading

    Discover the power of data science through 10 intriguing case studies, including GE, PayPal, Amazon, IBM Watson Health, Uber, NASA, Zendesk, John Deer, etc. ... In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve ...

  14. Introduction to Management Science: A Modeling and Case Studies

    The Sixth edition of Introduction to Management Science focuses on business situations, including prominent non-mathematical issues, the use spreadsheets, and involves model formulation and assessment more than model structuring. The text has three key elements: modeling, case studies, and spreadsheets. In addition to examples, nearly every chapter includes one or two case studies patterned ...

  15. Adapting the scrum framework for agile project management in science

    An interview protocol, designed as the third and main source of data for the study, was used for seventeen semi-structured interviews with researchers (nine men and eight women) from diverse disciplines and institutions who have experience with the adoption of agile practices in their projects (Table 1).The interview questions were developed with the goal of obtaining different perspectives on ...

  16. What Is Management Science? + How to Enter This Field

    What is management science? Management science is the study of problem-solving and decision-making in organizations. You can think of it as applying the scientific method to management, enabling managers to make decisions for an organization and improve its performance. For example, health care facilities can use management science to determine ...

  17. Introduction to Management Science 7th Edition

    Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets 7th Edition is written by Frederick Hillier, Mark Hillier and published by McGraw-Hill Higher Education. The Digital and eTextbook ISBNs for Introduction to Management Science are 9781265461522, 126546152X and the print ISBNs are 9781260716290, 1260716295.

  18. Introduction to Management Science

    Introduction to Management Science: A Modeling and Case Studies Approach with Spreadsheets, 6/e. Frederick S. Hillier, Stanford University Mark S. Hillier, University of Washington. To learn more about the book this website supports, please visit its Information Center ...

  19. What Is a Case Study?

    Revised on November 20, 2023. A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research. A case study research design usually involves qualitative methods, but quantitative methods are ...

  20. Introduction to Management Science:A Modeling and Case Studies Approach

    Introduction to Management Science, 5e, offers a unique model approach and integrates the use of Excel. Through this approach students are better able to grasp the essential concepts covered in the course and see their utility. ... Each chapter includes a case study that is meant to show the students a real and interesting application of the ...

  21. Management science

    Management science. Management science (or managerial science) is a wide and interdisciplinary study of solving complex problems and making strategic decisions as it pertains to institutions, corporations, governments and other types of organizational entities. It is closely related to management, economics, business, engineering, management ...

  22. Introduction to Management Science and Business Analytics: A Modeling

    Hiller's Management Science and Business Analytics: A Modeling and Case Studies Approach with Spreadsheets, 7e in its newest edition introduces both Management Science and Business Analytics in chapter 1 emphasizing the close relationship between them.The new Chapter 2 is an overview of how MS and BA professionals analyze problems and their similarities, chapter 3 is a new chapter on the role ...

  23. A generative AI reset: Rewiring to turn potential into value in 2024

    It's time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI's enormous potential value is harder than expected.. With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI ...

  24. [2403.13367] Quantifying the Aggregate Flexibility of EV Charging

    Electric vehicles (EVs) play a crucial role in the transition towards sustainable modes of transportation and thus are critical to the energy transition. As their number grows, managing the aggregate power of EV charging is crucial to maintain grid stability and mitigate congestion. This study analyses more than 500 thousand real charging transactions in the Netherlands to explore the ...

  25. Pages 1-52 (March 2024)

    Laparoscopic management of remnant gall bladder with stones: Lessons from a tertiary care centre's experience Gilbert Samuel Jebakumar, Jeevanandham Muthiah, Loganathan Jayapal, R. Santhosh Kumar, ...

  26. Introduction to Management Science:A Modeling and Case Studies Approach

    Introduction to Management Science:A Modeling and Case Studies Approach with Spreadsheets, 5/e Frederick S. Hillier, Stanford University Mark S. Hillier, University of Washington

  27. A fuzzy interval dynamic optimization model for surface and ...

    The allocation of water in areas which face shortage of water especially during hot dry seasons is of utmost importance. This is normally affected by various factors, the management of which takes a lot of time and energy with efforts falling infertile in many cases. In recent years, scholars have been trying to investigate the applicability of fuzzy interval optimization models in attempts to ...

  28. Brain Sciences

    Quantifying saccadic eye movements can assist in identifying dysfunctional brain networks in both healthy and diseased people. Infrared Oculography is a simple and non-invasive approach to capturing and quantifying saccades, providing information that might aid in diagnosis and outcome assessments. The effect of spinal manipulation on quantified saccadic performance parameters has not been ...