Five years ago, the Rana Plaza factory in Bangladesh collapsed killing more than 1,100 workers and revealing the horrific working conditions that garment workers endure across the developing world. Still, to this day apparel makers are trying to get a grasp on the social responsibility practices being used in their supply chains.
MIT Sloan Associate Professor, Yanchong (Karen) Zheng
To create a transparent supply chain requires a company to both learn what is occurring in their supply chains and disclose relevant information to consumers. Gaining supply chain visibility is the often-overlooked aspect of supply chain transparency. It is a complicated and time-consuming endeavor—and the benefits to a company’s bottom line are not always entirely clear. Do customers really care? And even if they do care, are they willing to reward a company for its efforts?
According to our research, the answers are yes and yes. A majority of customers value insight into a company’s supply chain—and some are even prepared to pay a premium for greater visibility.
A number of forward-thinking companies such as Nike, Levi’s and Patagonia, for instance, have long published their lists of suppliers, and have also put mechanisms in place to ensure their products are manufactured in a responsible manner. For them, supply chain visibility is an absolute must: a social good that safeguards human rights, but also provides information that customers deserve to know. Read More »
Paul Michelman, editor-in-chief of MIT Sloan Management Review
MIT Sloan Lecturer Ben Shields
From MIT Sloan Management Review
Almost every team’s fans can point to a high-profile, free-agent signing that fell flat. Sometimes the reasons for the player’s underperformance are clear — injuries, for example. But often, it’s harder to pinpoint the cause. In those cases, we tend to point to lack of effort as the culprit. This is what researchers call shirking. According to Richard Paulsen, who presented his paper, “New Evidence in the Study of Shirking in Major League Baseball,” at the Sloan Sports Analytics Conference, “Shirking occurs when an employee exerts effort … that is suboptimal in the eyes of the employer.” Paulsen speaks with us about his research on shirking and his belief that the Phillies are going to regret at least one free-agent signing from this off-season.
Fellow, MIT Center for Digital Business, Tom Davenport
From BizEd Magazine
The rise of data analytics is one of the hallmarks of 21st-century business. By the turn of the century, companies had been accumulating data in various transaction systems for several decades, and many desired to analyze the data to make better decisions. Their interest intensified in the early 2000s as they saw the great success of online firms from Silicon Valley, many of which were highly analytical.
In fact, during the mid-2000s, I conducted research showing that some companies were “competing on analytics”— that is, emphasizing their analytical capabilities as a key element of their strategies—and that those companies tended to outperform other firms in their industries. Information about analytics even made it into popular culture, especially through books such as Moneyball, which was also a successful movie. Both depicted the way the Oakland A’s of California built a winning baseball team through targeted data analysis.
The American engine of progress and prosperity is in serious trouble. Innovation has stalled. The number of good, middle-class jobs is dwindling. Wealth and opportunity are increasingly concentrated in a few coastal megacities. And cultural divides are widening. How do we turn this tide?
The answer lies in science — specifically, government-funded science. Investment in science is the ultimate pro-growth policy: It leads to more invention, higher productivity and broad-based economic development.
According to our research, if the U.S. government were to boost funding by $100 billion per year with strategic, geographically dispersed investments and initiatives, the result would be roughly 4 million new jobs.
Pamella Gonçalves, MIT Sloan Management alumna, MBA ’17.
The practice of Urban Analytics is taking off within the real estate industry. Data science and algorithmic logic are close to the forefront of new urban development practices. How close? is the question — experts predict that digitization will go far beyond intelligent building management systems. New analytical tools with predictive capabilities will dramatically affect the future of urban development, reshaping the real estate industry in the process.
In his introduction to ‘Smart Cities,’ Anthony Townsend raises the issue clearly: “Today more people live in cities than in the countryside, mobile broadband connections outnumber fixed ones and machines outnumber people on a new Internet of Things.” Yet neither the glossy marketing of major IT players such as IBM and Cisco nor the dystopian theories of critical scientists like Adam Greenfield admit that the digital revolution washing over cities has yet to be fully evidenced. Instead, over the last decade we have witnessed the slow emergence followed by strong growth of the computational paradigm applied to urban planning and real estate.