The Center for Business Optimization, created less than two years ago, commits some of IBM's brainiest people to complex business problems. In a break from traditional business management or computing services, the Center of Business Optimization relies heavily on advanced mathematics in consulting engagements.
That is, consultants use modeling and analytical techniques to find quick ways to solve a problem, such as spotting fraud on medical claims or devising the optimal retail marketing plans.
To deliver the service, IBM may crunch mountains of data on its high-end servers--enabling it to take on tougher problems--or pull from itsto build and integrate a finished application.
This combination of business consulting services and deep technical expertise is perhaps the optimal mix for IBM, something that few, if any, other services or IT companies can provide.
Indeed, IBM CEO Sam Palmisano has made a priority of high-margin, "transformational" businessalong with sophisticated technology--the polar opposite of .
At the head of the Center for Business Optimization is William Pulleyblank, who as an IBM researcher contributed to the company'ssupercomputer initiative.
Pulleyblank runs the unit like a start-up within the IBM behemoth. Although it's relatively small, he thinks the center is "central" to IBM's long-term plan.
One of IBM's goals is to reuse the "assets"--software and consulting know-how--the company creates in one customer engagement in others. That practice, now being explored at the Center for Business Optimization, could speed up Big Blue's ability to deliver services and improve its margins.
Pulleyblank spoke to CNET News.com about the center and its mission within IBM.
Q: What was the founding idea of the Center for Business Optimization?
Pulleyblank: We started using math to solve business problems before I joined IBM Research in 1990. Research was doing this for a long time, saying, "If we take this mathematical capability and apply it to problems, could we schedule airlines more effectively?" On the technical side, as long as people have been using math to solve problems, IBM has been doing it.
The real change that took place was about two years ago when Sam Palmisano pulled together a group of senior leaders in the company and said, "If we're going to do some things, which will enable the company to grow revenue, what can we do?"
What he said was there are really two ways you can do that--either you compete in a market where you're already playing, and we will continue to do that, or you find a new area where there is a market which hasn't been really exploited. If you can move into that, that's got a real appeal to it. This (area) was identified.
The observation was that for years we've been collecting digital data, we work with a lot of business groups within clients, why can't we take this kind of mathematical capability--which had been developed in research--and now apply it as a business tool? The question was, can we find a way to deliver it to businesses so they can make use of it?
It's simple when I say it, but what we're going to do is a solution which will be customized and integrated with the operation by people who know and do that.
How does the Center for Business Optimization differ from the rest of IBM's business consulting services?
Pulleyblank: If you look at a standard services model for consulting, it's all labor-based--time and material. Basically, if I work for you, I'm getting smart people to commit to work for you for a certain number of hours and do something. You'll pay us based on that number of hours.
What we're offering instead is services-led, asset-based. What that means is about half of what you'd pay me would be for a software asset that would be installed in your system. And the other half of it would be the services that go around it, which would customize it, would integrate it with your operations. It sort of combines the product with the normal services engagement.
Where does the Center for Business Optimization come into the picture?
Pulleyblank: No. 1, we own and manage the assets. So, if you look at these assets like our fraud and abuse management system, this is a software asset that we own, that we are continuing to develop and enhance it. We support it; we also have the domain experts that will go out and talk to the clients, work with the clients on the delivery of it, and do the integration of it into their systems. And we'll also do the selling of it.
At the same time, on an engagement, we'll technically work with another part of IBM's consulting organization. We will use consultants from the other practices in IBM to handle the installation.
So the point is that you can now reuse what you did in another engagement, at least a portion of it?
Pulleyblank: That's right, absolutely, and for us that's a great advantage because No. 1, it really reduces the risk of the engagement. We're not developing 100,000 lines of code that we're going to deliver to a client and somehow hope that it all works. This asset is something we had used before; we know it works. We also deliver a capability to the client that we couldn't possibly build for them in any reasonable economic way. You look at this fraud detection, all the stuff going into that. If one client wanted to pay for that entire development, it would be prohibitively expensive because there's so much in there because it's developed over eight years now.
Pulleyblank: Well, first of all it's all economics. If a customer wants, I'd be more than happy to give him an exclusive on all the stuff I developed. What customers really want is to get the advantage in pricing that they get from viewing it more as a replicable product.
Your charter also involves tapping into IBM researchers. How do they get involved?
Pulleyblank: If there's an aspect of a problem that comes up that we haven't seen before, we'll get a research team working with that. If we need better performance, the research teams will look at that. If we need some of the architectural capability kinds of things, they'll look at that. We may need some kind of visualization that will help the client understand this particular relationship. We use them really as a lot of the "thought engines" people behind it.
How does applying advanced math to hard business problems work in practice?
Pulleyblank: What you want to do is look at the historical data that a company may have. We'll take a look at all the data that's there and we can analyze the data and say, this is interesting because there was a marketing campaign that took place here. What's interesting is that two weeks after there was quite a shock. You actually got a real uptake in enquiries about it.
Now, once you've got the dynamics of the company there, then you can optimize with respect to that, and say OK, now we think we know how (the system) behaves. Therefore, here's the most cost-effective way to spend your budget to get the kind of results you want.Is your job to act as a sort of a SWAT team to handle really hard problems?
Pulleyblank: In fact, when we set up the mission of this center, part one of the mission was to solve really hard problems. It's the "Star Trek" part--go boldly where no one's gone before. That's cool and that's what the research people love.
But the other part is absolutely critical. I spend more time worrying about this than any other part, and that's to make it real. Find a way to take that particular capability and deliver it to clients so that we can get into their business operations effectively. That's been the biggest obstacle to the use of these kinds of methods in businesses until now--they have been too hard to use, too inflexible. So for a company to optimize the marketing campaign, first thing that you should do is hire 25 Ph.D.s and leave them alone for three years, after which they may know what to do.
If that's what they do, they'll never get to it. So, we're trying to short-circuit that and find ways to speed it up. Then the third part is simply to turn it into a sustainable, profitable business for IBM.
Pulleyblank: There is a company called SmartOps, that is a small company that does inventory optimization, which came from Carnegie Mellon University. They've done some inventory optimization with some major companies. One particular problem had 70,000 SKUs (stock keeping units). Doing the inventory models on it took six hours for them to run it. Because they have a university background, I think they were wondering what would happen if we put it onto a Blue Gene supercomputer. So we enabled them to do that, and they just used one rack of Blue Gene and it went down from the six hours to 17 seconds, which solved the problem.
If it takes you six hours to solve this problem, you do it overnight and you come in the next day, and hope you got an answer. If it's 17, 18 seconds, you do it, you look at the answer, you change something, you do it again. So all of a sudden these become operational, and that's the other enormous change. It used to be that this math stuff was used for planning, which meant you couldn't use it in operations because you couldn't do it fast enough. But now we're saying we can do it fast enough that it'd be part of the operations.
IBM is creating development centers around the world, including India, with the goal of creating replicable "solutions." Is that part of your plan?
Pulleyblank: When I'm working with a client directly somewhere in the U.S., for example, it will be mainly people on the ground who are working with the client on all kinds of areas. What happens then is as we start to turn it into a solution, which we're going to replicate and reuse, we'll architect it and get it rolling for the teams in India who'd be the ones who would do a lot of the development for us on that. So in particular, some of the work we want to do to make it effectively consistent with the standards software stack, standard architecture, this will be the kind of functionality which they'll be able to do for us very effectively. Now, we'll see how that evolves, but that's where we're going initially at least.
Isn't this reuse idea kind of the holy grail of the IT industry--making software production more like a manufacturing processing by reusing components?
Pulleyblank: Think of it. Who is the biggest company in the software industry? Microsoft. How do you become the world's richest person selling something that's only $150 (like Windows XP)? You sell a lot of them. You see the software industry has been largely built around high volume, lower unit price.
When we go into it, we're going the other direction. If we look at these services-led asset-based solutions, we're not going to sell millions of things, and they're going to be much more expensive than your few hundred dollars, or few thousand dollars, just for the asset itself. It's quite a different model because it's different types of problems that it's going after. So, in some sense, there's a whole part of the software industry that is on a completely different trajectory from we're doing
Doesn't that limit your market, though, to the really hard problems in the world? Presumably, that would mean a smaller set of customers.
Pulleyblank: It may not be everybody, but that's OK. I hope some of the things that we develop will in fact evolve into standard software products at some point, and be very broadly used. And when that happens, it'll be a group like the software group within IBM that will take them up, and that's fine with me. The spreadsheet is an example that has become absolutely mass market. Even though it's a very sophisticated thing, it evolved into that.
That's what we do--that's our particular center. The thing I stress is we do want to get the hard problems, but we have to do it in a way so that people can use it, and it's that dynamic which really drives a lot of what we do.