O.R. and Analytics in the News (2024)

O.R. and Analytics in the News (1)

Fast Company, December 24, 2003

New detection methods fueled by artificial intelligence could give anti-trafficking efforts a much-needed boost in accuracy and efficiency.

Imagine an elite professional services firm with a high-performing, workaholic culture. Everyone is expected to turn on a dime to serve a client, travel at a moment’s notice, and be available pretty much every evening and weekend. It can make for a grueling work life, but at the highest levels of accounting, law, investment banking and consulting firms, it is just the way things are.

Except for one dirty little secret: Some of the people ostensibly turning in those 80- or 90-hour workweeks, particularly men, may just be faking it.

Many of them were, at least, at one elite consulting firm studied by Erin Reid, a professor at Boston University’s Questrom School of Business. It’s impossible to know if what she learned at that unidentified consulting firm applies across the world of work more broadly. But her research, published in the academic journal Organization Science, offers a way to understand how the professional world differs between men and women, and some of the ways a hard-charging culture that emphasizes long hours above all can make some companies worse off.

Even pirates have their redeeming qualities.

The counterfeiter might be a profit-sapping scourge to many designers, but recently published research from a trio of academics shows that fakes can also push brands to up their game — particularly in terms of aesthetics.

A study published in Market[ing] Science academic journal looked at 31 brands that sold fashion leather and sport shoes in China from 1993 to 2004. The Chinese market proved to be something of a petri dish to the researchers, since it saw a major influx of counterfeits after 1995, when the government pivoted away from the enforcement of footwear trademarks to respond to problems in other sectors, including gas explosions and food poisonings.

“Established companies don’t sit idly by while they are copied shamelessly,” said Yi Qian, a professor at University of British Columbia Sauder School of Business, who cowrote the study. “They react by improving their products to set themselves apart from their illegal competitors.”

As the debate continues over whether college student-athletes should be paid for their on-field performances, a new study from Harvard Business School reveals just how much intercollegiate football and basketball programs contribute to a school’s bottom line.

The quantitative link between game day and payday is courtesy of Assistant Professor Doug J. Chung, who reviewed 117 schools with Division I football and basketball teams, matching athletic performance with revenue flow covering an 11-year period. The findings were jaw-dropping—winning just one more football game in a season, for example, could bump revenues by as much as $3 million for a high-powered program like Alabama or Michigan.

Chung details the correlation between wins on the field and wins for a school’s piggy bank in his paper, How Much Is a Win Worth? An Application to Intercollegiate Athletics, forthcoming in Management Science.

Swiss-owned Syngenta, which has a major presence in North America, celebrated a major award at Iowa State University Nov. 13 calling for a math revolution in agriculture.

Attended by plant breeders, ag graduate students and college faculty at the Scheman Building on ISU's campus, Syngenta officials explained how it has incorporated advanced analytics into its soybean breeding procedures with assistance from ISU faculty and others.

The team's success won Syngenta the 2015 Franz Edelman Award for achievement in operations research and the management sciences in mid-April.

Analytics continues to bring dramatic change to the healthcare industry in the United States and other countries, offering advances and challenges for the year ahead. Following are 10 trends to chart in 2016.

O.R. and Analytics in the News (3)

Rarely in the published research about job-hunting does a new perspective on methods emerge. Job seekers have to avoid restricting restricting their search to any one method, because they can’t predict the one that will produce. However, fresh perspectivecomes from a new study published in the INFORMS journal Management Science.

O.R. and Analytics in the News (2024)


What are the 4 types of analytics? ›

The four forms of analytics—descriptive, diagnostic, predictive, and prescriptive—help organizations get the most from their data.

What is operations research analytics? ›

Operations research analysts advise managers and other decision makers on the appropriate course of action to solve a problem. Operations research analysts use mathematics and logic to help organizations make informed decisions and solve problems.

Are analytics and operations the same? ›

Though similar in definition, and there are instances of overlap, Analytics and Operations Research are actually two unique but related fields. Analytics helps realize business objectives by analyzing data to create predictive models for forecasting and optimizing business processes for enhanced performance.

What is the Dow Jones news analytics? ›

Financial Analysis – Investors can use news analytics to monitor the performance of existing investments, identify new investment opportunities, anticipate and mitigate potential portfolio risks, hedge against events, uncover trends and analyze broader economic developments.

What are the 3 common categories of data analytics? ›

Descriptive, predictive and prescriptive analytics.

What is the most common type of analytics? ›

Descriptive analytics is one of the most basic level of classification of analytics used by almost 90% of organizations. It focuses on answering "What has happened?" by analyzing real-time and historical data.

Is analytics a lot of math? ›

While data analysts must be adept with numbers and can benefit from having a basic understanding of math and statistics, much of data analysis simply involves following a series of logical procedures. People don't need to have a lot of mathematical expertise to excel in this field.

What is an example of operations analytics? ›

Following are some of the common operational analytics use cases:
  • Banks use Operational Analytics to provide suitable Products. ...
  • Operational Analytics is used for Preventive Maintenance in Manufacturing. ...
  • Operational Analytics in Supply Chain Management. ...
  • Operational Analytics in Marketing.

Is analytics same as AI? ›

Data analytics focuses on analyzing historical data to gain insights and make informed decisions. AI analytics combines traditional analytics with artificial intelligence techniques to automate and enhance the analytics process, allowing for more advanced analysis, prediction, and decision-making capabilities.

What does Dow stand for? ›

The Dow Jones Industrial Average, or the Dow for short, is one way of measuring the stock market's overall direction. It includes the prices of 30 of the most actively traded stocks. When the Dow goes up, it is considered bullish, and most stocks usually do well.

What is DAX and Dow Jones? ›

The DAX Stock Index is a very concentrated index holding only blue-chip companies that are very large and successful. It is very similar to the Dow Jones Industrial Average (DJIA), which is a price-weighted index that includes 30 very large U.S. companies.

What is the Dow Jones vs S&P? ›

Key Takeaways

The DJIA tracks the stock prices of 30 of the biggest American companies. The S&P 500 tracks 500 large-cap American stocks. Both offer a big-picture view of the state of the stock markets in general.

What are the 4 types of digital analytics? ›

Analytics is a broad term covering four different pillars in the modern analytics model: descriptive, diagnostic, predictive, and prescriptive. Each type of analytics plays a role in how your business can better understand what your data reveals and how you can use those insights to drive business objectives.

What are the 4 Ps of data analytics? ›

The topics dominating discussion at the enterprise digital analytics table are prioritization, personalization, people and perspective. Few fields change as fast as digital.

What is 4 big data analytics? ›

There are four main types of big data analytics—descriptive, diagnostic, predictive, and prescriptive. Each serves a different purpose and offers varying levels of insight.

What are the 4 levels of learning analytics? ›

The Levels of Learning Analytics

We define four levels of these analytics: measurement, evaluation, advanced evaluation, and predictive and prescriptive analytics.

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