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Focus: Supply Chain Trends/Issues

Feature Article from Our Supply Chain Trends and Issues Subject Area - See All

From SCDigest's On-Target E-Magazine


Oct. 19 , 2011

Supply Chain News: IBM Claims Software Breakthrough with New "Multi-Objective" Supply Chain Optimization Technology

Result is a Tradeoff Curve Across Objectives that was Very Difficult to Achieve Previously, Allowing Companies to Get Better Insight for Decision-Making


SCDigest Editorial Staff


Somewhat quietly at the CSCMP Conference in Philadelphia two weeks ago, IBM announced what is calls a substantial breakthrough in supply chain optimization technology, saying it is the first to provide "multi-objective" optimization capabilities that will enable companies to better understand trade-offs between different objectives, such as cost and service.

The technology innovation was announced as simply one of a number of other new features in the latest release of IBM's LogicNet Plus XE, a network optimization tool IBM picked up when it acquired ILOG in 2009. However, the potential impact on supply chain optimization from this development could actually be quite significant. IBM says it plans a more formal, focused announcement relative to the news soon.

SCDigest Says:


Watson said that as the result of this new technology, optimization software can now automatically build a trade-off curve that shows how different objectives play off against each other.

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According to Dr. Michael Watson of IBM, during a video interview with Supply Chain Digest editor Dan Gilmore, the multi-objective capability is about being able to optimize across two or more objectives at the same time.

"Executives have long known when they are making decisions in the supply chain, it's not just cost, it's not just service, it's not just capital investment, it's a combination of all these things that drives their decisions," Watson said. "What we are now able to do with some innovative mathematical optimization techniques is to analyze these different objectives all at the same time."

Watson said that as the result of this new technology, optimization software can now automatically build a trade-off curve that shows how different objectives play off against each other (see graphic in the bottom half of this article).

"In the past, companies had to pick a few points and kind of manually build this type of curve," Watson said. "Now we can automatically build the curve. It provides some additional benefits beyond just understanding the trade-offs between different objectives. It also gives you some idea of the range of the span. Does one objective, for instance, range from $10 million to $50 million, or does it range from $25 million to just $25.2 million? That gives me some insight. The shape of the curve also gives me some insight."

He said for example these trade-off curves can allow companies to see areas where there might be a big step jump between one part of the curve and an adjacent part, or easily identify where there might be an exponential type rise say in an objective such as cost as it approaches a 100% service level.

In the end, it will make for better and more informed decisions in the supply chain, Watson said.

The innovation is first being rolled out in IBM's network optimization tool, but should soon also appear in Big Blue's inventory optimization and factory scheduling software, as well as its market- leading optimization tool kit, CPLEX, which is embedded in many other supply chain planning software solutions from other vendors and used by individual companies to solve specialized supply chain optimization problems.


Watch Full Video Interview




What are the Applications?

Watson said there are many potential uses, but the most obvious is better understanding the cost versus service trade-off.

He said a recent customer, for example, wanted to better understand how logistics costs traded off versus the percent of customers within 200 miles of DCs in various network options, which was the company's service target.

(Supply Chain Trends Story Continued Below)





"They wanted to see that curve and look at the cost impact of having say 80% of the customers within 200 miles of each DC versus say 85%," Watson said.

Another popular use case will be comparing the total cost of a supply chain network versus the capital investment required to get there. A slightly less than optimal network in terms of operating costs may in some cases require substantially less capital investment, making it the best total decision. Or, Watson said, you could look at what the optimal network might be given different levels of capital investment ($10 million versus $20 million versus $30 million, etc.)


New IBM Optimizer Automatically Builds Trade-Off Curves Beween Objectives



Watson said he believes that more and more business decisions will be made using optimization technologies, and that this sort of innovation will be key to extending the range of problems to which optimization technology can be applied.

He adds that a combination of improved optimization engine capabilities, much faster, more powerful computers, and cloud computing will dramatically increase the use of optimization in businesses. The cloud, for example, will allow business to simply call out to an optimization engine on the Internet to continually submit data and receive back optimized answers, rather than installing the optimization engines internally.

Before this development, Watson said companies would have to run a single objective optimization, for example on cost, and then manually runs scenarios with a few data points on different service levels. Besides the manual nature of the process, which takes time, the result is really a very incomplete curve, and risks the result not showing important step changes or other insights between the few data points selected, Watson says.

"It's a tedious process, and so in the past you wouldn't want to do more than one or two of these curves, "Watson added. "Now, you can easily run 10 or 20 of them, which will give you a lot more insight.

SCDigest expects to follow up on this apparent breakthrough in coming months.

Does this multi-objective optimization engine seem like an important breakthrough to you? Why or why not? How can you see it improving decision-making? Let us know your thoughts in the Feedback area below.

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