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Here s how the Boeing system works: Boeing takes responsibility for purchasing, inventory management, and logistics for their airline customers expendable airframe parts. All airframe parts are forward-deployed by Boeing at or near the airline s point of use. That means that the parts are stored either in a Boeing partner s warehouse or actually at one of the airline customer s parts departments. Boeing and other suppliers own these airframe parts until they are actually used. An airline pays for parts only as it uses them. This signi cantly reduces the airline s inventory holding costs. A supply chain management system serves as the command center for the network, monitoring the parts use of customer airlines and allowing suppliers to better forecast demand and plan production accordingly. The existing Boeing spare parts distribution system, with seven regional distribution centers around the world, is the backbone of the network. In addition to Boeing s proprietary parts, these distribution centers stock many of the other 2000 suppliers parts, creating a single parts-distribution channel. This system helps airlines reduce costs and frees up signi cant amounts of capital that were formerly tied up in inventory. It enables

Small samples of market data are unlikely to be representative of the universe from which they are drawn: consequently, they will probably differ significantly from other samples obtained from the same universe Applied to a small development sample, an optimizer will faithfully discover the best possible solution The best solution for.

suppliers to bene t from the ef ciencies that come from better demand forecasting and using a single distribution channel.

the development sample, however, may turn out to be a dreadful solution for the later sample on which genuine trades will be taken. Failure ensues, not because optimization has found a bad solution, but because it has found a good solution to the wrong problem! Optimization on inadequate samples is also good at spawning solutions that represent only mathematical artifact. As the number of data points declines to the number of free (adjustable) parameters, most models (trading, regression, or otherwise) will attain a perfect tit to even random data. The principle involved is the same one responsible for the fact that a line, which is a two-parameter model, can always be drawn through any two distinct points, but cannot always be made to intersect three arbitrary points. In statistics, this is known as the degrees-of-freedom issue; there are as many degrees of freedom as there are data points beyond that which can be fitted perfectly for purely mathematical reasons. Even when there are enough data points to avoid a totally artifact-determined solution, some part of the model fitness obtained through optimization will be of an artifact-determined nature, a by-product of the process. For multiple regression models, a formula is available that can be used to estimate how much shrinkage would occur in the multiple correlation coefficient (a measure of model fitness) if the artifact-determined component were removed. The shrinkage correction formula, which shows the relationship between the number of parameters (regression coefficients) being optimized, sample size, and decreased levels of apparent fitness (correlation) in tests on new samples, is shown below in FORTRAN-style notation:

The systems described in this chapter may not work for your situation. The payoffs from letting customers behind the counter can come only if The customers actually want to come behind the counter and will use your new Web site to do so. The customers gain something that is of value to them from using your new system. The costs of setting up the system are not too high in relation to the payback. Some companies have spent millions of dollars on such systems with no hope of payback. Boeing s system was very, very expensive. But the company was building market share in a $7 billion market. The system will have a measurable impact on your customers retention and spending rates. How can you know that You must run little tests to nd out. You have tested everything on a small scale before you make big mistakes.

In this equation, N represents the number of data points, P the number of model parameters, R the multiple correlation coefficient determined for the sample by the regression (optimization) procedure, and RC the shrinkage-corrected multiple correlation coefficient. The inverse formula, one that estimates the optimizationinflated correlation (R) given the true correlation (RfJ existing in the population from which the data were sampled, appears below:

For every piece of information that you have stored in your company computers, gure out if it would be of interest to some of your customers. If so, nd a way to let them access this information directly on the Web without human intervention. Let them come behind the counter. Set up special Web sites for your best customers. Provide reports to your customers on what they are buying and who is buying it. Provide volume-pricing arrangements to induce your customers to use your new Web site for ordering.

These formulas, although legitimate only for linear regression, are not bad for estimating how well a fully trained neural network model-which is nothing more than a particular kind of nonhnezu regression-will generalize. When working with neural networks, let P represent the total number of connection weights in the model. In addition, make sure that simple correlations are used when working with these formulas; if a neural network or regression package reports the squared multiple correlation, take the square root.

The two major classes of genes critical for cancer development are oncogenes and tumor suppressor genes Oncogenes stimulate cell growth and proliferation; tumor suppressor genes slow.

Before you do anything, use the tables in this chapter to determine whether your new system will be pro table. If it will be, use these tables to sell your concept to your management.