pushed out top management and brought in Louis Gerstner, then the head of RJR Nabisco, to serve as CEO. Though Gerstner took steps to break up IBM’s sclerotic bureaucracy, he chose to keep the company in one piece. He said he believed that IBM’s size, which enabled it to focus resources on big problems for large corporate and government customers, was a valuable asset that should be preserved.
A key part of Gerstner’s strategy was to unify and expand IBM’s global-services business. Paul Horn, who headed IBM Research during part of that time and is now senior vice provost for research at New York University, saw that under the circumstances, the labs could easily be viewed as a costly luxury. With services growing, he says, “if research wasn’t contributing, you could imagine someone in the future saying, ‘You don’t need to be so big.’ ” Horn, a physicist, helped convince Gerstner that IBM’s research division could play an important role in his strategy by working with customers to solve their problems. He began pushing his thousands of researchers, including the mathematicians, to start working on projects that could be useful to the services business. The motive was simple, he says: “Survival.”
For the mathematicians, the shift was a natural one. Dietrich says they had frequently worked with IBM’s own manufacturing plants on scheduling problems and logistical issues, though the results were usually considered proprietary. And they had already begun getting more involved in business operations, in part because it provided them with the large data sets that they needed for modeling. Historically, stochastic optimization had been limited by the sheer amount of computing required to deal with multiple variables. But as computer power exploded and researchers began to use massively parallel processors, they were able to manipulate much more data.
IBM Research mathematician Baruch Schieber recalls going to a Brazilian steel mill and finding that production schedules were being drawn up on whiteboards. Surely, mathematical models could do it better, he thought. He was especially interested in the issues involved in scheduling production runs for different varieties of steel. Though it’s cheaper to do long runs of one type of steel, sometimes customers need several different types immediately, so the mill has to do short runs. “Mathematical modeling is quantifying things that usually aren’t quantified,” he says–such as the tradeoff between cost and customer satisfaction. Early in a contract period, Schieber discovered, the mills wanted to optimize their schedules for maximum efficiency and minimum cost. At the end of the period, when the contract was up for renewal, they sought to focus more on improving satisfaction. Similar issues arise with airlines. Schieber says, “We ask managers: do you want to minimize crew costs or fuel, or do you want to maximize customer satisfaction?”
William Pulleyblank, who headed IBM’s math department in the 1990s, had urged the company even then to make a business out of analytics. “A lot of companies tried to do this,” he says. “It was seen as a pure product play–package it and sell it.” However, he adds, it became clear that IBM didn’t have a good way to sell the mathematicians’ skills to clients. He concluded that many companies’ needs were so specialized that designing a general-purpose software package wouldn’t be profitable–but software designed for particular businesses wouldn’t be in high enough demand. At the same time, IBM didn’t want its researchers to become consultants. The mathematicians didn’t want to do it, and they weren’t trained for relating to customers. “I realized the challenge wasn’t the math,” says Pulleyblank, who is now a vice president in the business analytics and optimization group. “It was how to make it a business.”
The path to an analytics business became clearer in 2002, when IBM paid $3.9 billion to acquire the consulting business of PricewaterhouseCoopers. Ginni Rometty, who spearheaded the deal and now heads IBM’s sales operations, recalled Pulleyblank’s idea. She thought that PWC’s consultants could expand IBM’s service offerings beyond IT; its researchers could be touted as a unique source of advice to client companies on marketing, human resources, and logistics. Each fall, when IBM’s sales teams start forecasting upcoming business, the consultants identify critical problems that are likely to affect particular industries in the coming year. If those problems look like analytics issues, the consultants contact the business analytics and optimization team and ask whether IBM has worked on anything similar before. In many cases, the problems can indeed be addressed by adapting the company’s existing software products.
When existing software can’t do the job, the consultants turn to IBM Research for help. Sanjeev Nagrath, IBM’s global leader for supply-chain management, encountered such a situation last year when clients started asking how to reduce the carbon footprint of their supply chains. So, Nagrath says, they’re working with Research to come up with industry-specific models to deal with sustainability issues. And two years ago he worked with Dietrich to create a center for supply-chain
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