innovation in Beijing. There, Chinese mathematicians are part of a team working with companies such as Chinese shipping giant Cosco. The innovation center’s mathematicians helped IBM consultants model Cosco’s procedures and developed a plan that cut fuel costs 25 percent and carbon dioxide emissions 15 percent. Among other things, they recommended reducing the number of distribution centers from 100 to 40.
Not all clients trust the mathematicians’ contributions, as Schieber found out when he created a model that could be used to reschedule ships if supplies were temporarily halted by bad weather. He says it was much better than human schedulers at adjusting fleet movements and speeds to minimize disruption and fuel costs. But the customer wasn’t satisfied. “It was a black box,” he recalls. “The shipper said, this is our competitive edge. They wanted to understand it.” The shipping company finally implemented the model after IBM redesigned it so that it was not a fully automated system but an aid that human dispatchers could consult.
Some businesspeople argue that many decisions are best guided by gut reactions based on years of experience. They worry that depending on analytics will make business leaders indecisive when they don’t have an abundance of data. And a math-phobic public is suspicious that analytics-driven programs cut costs at consumers’ expense. IBM researchers point to the recent backlash against recommendations that annual mammograms be delayed until women are 50 because they don’t provide statistically provable benefits for younger women.
But Dietrich is more concerned that companies will fail to analyze the petabytes of data they do collect. When she met with the pharmaceutical company about its portfolio management strategy, for instance, the executives explained how they allocated spending according to their estimates of how likely each project was to succeed. “I asked them if they ever checked to see how well the estimates matched their results,” she says. “They never had.”
Dietrich and her researchers are now working to rewrite optimization algorithms to take advantage of massively parallel computers. The older programs were written to minimize the number of operations required. But now that thousands of processors can churn through vast data sets, she says, “the issue is to reduce [run] time.” Once the team is done, those optimization programs will be available to businesses whose stores of data are too large to be analyzed with single-thread computer programs.
The most interesting problems the mathematicians envision for future projects involve situations where a model must incorporate behavioral changes that the model itself has inspired. For example, Dietrich says, a traffic congestion system might use messages sent to GPS units to direct drivers away from the site of a highway accident. But the model would also have to calculate how many people would take its advice, lest it end up creating a new traffic jam on an alternate route. She says that understanding the way systems change as humans react to incentives is one of the big challenges for mathematical modeling.
Of course, it’s never going to be easy to accurately predict what people–or businesses–will do. But thanks to their insights as mathematicians and their access to IBM’s vast computing power, Dietrich and her colleagues are getting better at it. And now, other companies are paying for that skill.
William M. Bulkeley is a former Wall Street Journal reporter who is now a freelance writer in Boston.