Analytics & Performance

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Remapping the Last Mile of the Urban Supply Chain

  • Blog
  • Read Time: 6 min 

There are many reasons to believe we are on the crest of substantial progress with even the most challenging of last mile deliveries. Innovative models such as smart locker systems, the use of electric vehicles, and on-demand fleet services such as UberRUSH are being explored. The MIT Megacity Lab is helping identify customer-specific insights about how supply chains deliver products to urban customers and finds that autonomous delivery vehicles, while still years from wide-scale implementation, hold game-changing promise.

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Building a Better Car Company With Analytics

Using data and analytics to understand the complexities of modern business has become not only common, but essential. Gahl Berkooz joined Ford Motor Co. in 2004, eventually becoming head of data and governance and a member of the company’s global data insights and analytics skill team. Berkooz became acutely aware of how important analytics is to the company’s ability to thrive in the global marketplace. “What it boils down to,” he told MIT SMR’s Michael Fitzgerald, “is that we know how to make decisions. It’s about finding the opportunities to bring data and analytics to make better decisions.”

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Where Digitization Is Failing to Deliver

  • Interview
  • Read Time: 8 min 

It has become a truism that the pace of work is faster than ever, as digital technologies speed up communication and operational processes in a story of unending progress. But increased speed has not translated into increased rates of productivity growth. Since 2004, growth rates have slowed not just in the US but across the world. Chad Syverson, J. Baum Harris Professor of Economics at the University of Chicago’s Booth School of Business, explains what the implications are, and why the benefits of new technologies are not straightforward.

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Free Webinar: Foundations of Analytics Strategy

The 2016 MIT SMR/SAS Data and Analytics report, “Beyond the Hype: The Hard Work Behind Analytics Success,” finds that competitive advantage from analytics is declining — but that organizations achieving the greatest benefits have figured out how to ensure that the right data is being captured. In this webinar, the authors of the report explain how companies are making this transition and which are seeing the most success.

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Beyond the Hype: The Hard Work Behind Analytics Success

The 2016 Data & Analytics Report by MIT Sloan Management Review and SAS finds that analytics is now a mainstream idea, but not a mainstream practice. Few companies have a strategic plan for analytics or are executing a strategy for what they hope to achieve with analytics. Organizations achieving the greatest benefits from analytics ensure the right data is being captured, and blend information and experience in making decisions.

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Avoiding Analytical Myopia

Analytics offers managers a great way to fine-tune processes, but too many executives focus on metrics at the expense of the bigger picture. The blinders and focus that work well to optimize the details of a problem may prevent managers from seeing other options, and intense focus on a narrow measure can address only the well-specified puzzle — resulting in a myopic view of the problem. Executives who desire bigger breakthroughs need to encourage exploration.

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What Email Reveals About Your Organization

By studying data from email archives and other sources, managers can gain surprising insights about how groups should be organized and about which communications patterns are most successful. Anonymized analysis of internal information communication found that creative people, for instance, work more productively on projects with strong leaders than on collaborations without a clear leader. In addition, in many situations, groups of leaders taking turns worked better at sparking creativity.

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Overcoming Legacy Processes to Achieve Big Data Success

Most large corporations are saddled with fragmented analytical processes, limiting their ability to operate with agility, flexibility, and insight. As a result, larger firms are often challenged when it comes to innovation and responsiveness. But Big Data approaches that enabled the flexibility and rapid growth of newer, smaller firms are being adopted by mainstream corporations. The goal: overcome legacy challenges and introduce greater corporate speed.

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Telling Data’s Story With Graphics

At the alcohol beverage company Constellation Brands, graphic presentations of data are making it easier for sales people to see how they’re performing. In an interview with MIT Sloan Management Review, Joseph D. Bruhin, the company’s CIO, says that measuring marketing and sales efforts is a particular challenge in the alcohol industry — but one that his team has come up with a solution to. “Visibility of data is a critical piece,” he says. “We came up with a solution that’s really driven predominantly by information technology.”

Image courtesy of Flickr user janneke staaks https://www.flickr.com/photos/jannekestaaks/14391226325
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Why Managing Data Scientists Is Different

The process of managing a data science research effort “can seem quite messy,” writes MIT Sloan’s Roger M. Stein. That can be “an unexpected contrast to a field that, from the outside, seems to epitomize the rule of reason and the preeminence of data.” While businesses are hiring more data scientists than ever, many struggle to realize the full organizational and financial benefits from investing in data analytics. This is forcing some managers to think carefully about how units with analytics talent are structured and managed.

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Innovating with Airborne Analytics

Hong Kong’s premier airline is using a blend of data and know-how to guide its daily operations. In an interview with MIT Sloan Management Review, Cathay Pacific CIO Joe Locandro describes how the airline uses analytics to make decisions that balance data with what it knows from the field. “Analytics will give you statistical spreads, give you training, but you still need to have this thing called experience and insight,” he says.

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Once You Align the Analytical Stars, What’s Next?

You’ve figured out how to get the data, and how to make sure it’s good quality. You’ve hired the right people to put your data through the analytics wringer. Now you’ve got the results in your hands &mdash and you may not be sure what to do next. Consuming analytics effectively — and getting business value out of your analytics — is a challenge for many companies, and executives must get creative to increase their comfort level.

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Minding the Analytics Gap

While analytical skills are improving among managers, the increasing sophistication of analyses is outpacing the development of those skills. The resulting gap creates a need for managers to become comfortable applying analytical results they do not fully understand. A 2014 survey by MIT Sloan Management Review, in partnership with SAS Institute Inc., highlights the ways that companies can address this problem by focusing on both the production and consumption sides of analytics.

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Using Simulated Experience to Make Sense of Big Data

As data analyses get more complex, how can companies best communicate results to ensure that decision makers have a proper grasp of the data’s implications? Research has found that letting decision makers gain experience on the outcomes of different possible actions by interacting with simulations helps those executives make better decisions. Simulations narrow the often a large gap between what analysts want to share and what decision makers understand, and more clearly illustrate complex statistical information.

Image courtesy of Flickr user Tristan Martin (https://www.flickr.com/photos/mukumbura/4043364183)
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Why the Non-Superstar Might Be the Most Important Team Member

Hot shots get all the attention, but other team members can be the ones who make a group really tick. “Plus/minus” analysis, which is used by some professional sports teams, lets organizations understand, through data, not just individual performance but performance in context. Research by Thomas H. Davenport details how the goal is to understand how a team performs when one person is part of the mix, and when they’re not.

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Does Your Company Collect Data — Or Hoard It?

As it has become clearer that data offers value to companies, some organizations are tempted to take a “more is better” approach. But there’s a fine line between collecting data that offers value and hoarding data, which ultimately proves counterproductive. Ransbotham’s Three Laws of Data Collection offer guidance.

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What Businesses Can Learn From Sports Analytics

In professional sports, some teams are becoming sophisticated in using data to measure team and player performance, sports business and health and injury prevention. Sports teams’ use of analytics has much to teach other managers about alignment, performance improvement and business ecosystems. For instance, teams are beginning to assess performance in context, seeing how teams do with or without a particular player. This “plus/minus” analysis could be a valuable technique for many businesses as well.

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Collecting Business Value from Energy Data

On the website of energy analytics startup Pilio, CEO and cofounder Catherine Bottrill says that it is a combination of technology, economics and politics that are instrumental in environmental problem solving. However, there is another ingredient necessary, says Bottrill: People. Bottrill is combining behavioral science with energy analytics to help organizations better understand — and manage — their energy use.

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How Analytics is Giving Fashion a Makeover

Recommendation engine StyleSeek is relying on data and analytics to drive business and fashion decisions — every single one of them. With about 50,000 actively registered users — impressive considering the company recently came out of private beta — and close to 200 retailers on board including the likes of Nordstrom, Macy’s and Anthropologie, StyleSeek may have tapped a new approach to a longstanding industry.

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Analyzing Performance in Service Organizations

We can’t always trust our intuition about how employees will perform. Intuition can be misleading, or just plain wrong. So a growing number of savvy service businesses have investigated the use of a sophisticated linear programming technique called DEA, or data envelopment analysis. Authors H. David Sherman and Joe Zhu, who call DEA “balanced benchmarking,” write that the technique helps companies locate best practices not visible through other management methodologies.

Showing 1-20 of 42