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importance for which all CEOs and senior executives are responsible and accountable, and that is to optimize performance Optimizing performance means applying scarce resources to business processes under constraint and transforming them into highest yield and best use outcomes by managing people and enabling technical mechanisms (technology) All enterprises exist for a purpose, that is expressed in mission and value statements, goals, and objectives, otherwise summarized into business plans Once desired outcomes are identi ed, leaders organize resources into functions Functions identify the work that needs to be done to produce outcomes How the work is accomplished is de ned as processes, where process activities constitute proprietary differentiation Proprietary differentiation or a unique way of accomplishing things is achieved through a variety of means that begin with creative leadership:
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Selecting the right customers to service Selecting the right things to do Organizing activities Attributing activities with application of a superior mix of people and technology Applying scarce resources in an optimal manner Structuring the balance of consequences such that doing the right things the right way is rewarded and deviations are dissuaded Ensuring that customers receive valuable results Assuring stakeholders that the enterprise is performing optimally
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Data is at the heart of each of these management activities and that is why we focus on data as a principal contributor to optimizing performance There are so many moving parts in an enterprise that it is unfathomable that modern executives would attempt to manage without techniques to keep track of them, but many do That condition is unacceptable in the highly automated world of the 21st century because resources are exceedingly scarce and risks are too high to operate intuitively One of the greatest barriers to realizing the full bene t of automation is from people inserting themselves with manual intervention, usually unintentionally They need and want data to perform better, but it can t because they did not plan and prepare suf ciently for the moment of need For enterprises to perform optimally, executives must insist on better data planning, preparation, and engineering For our case, we use data sets that have been used previously for comparing a number of techniques for classi cation The data sets consist of 1200 data samples Each data sample consists of three attributes and has 100 observations equally split between two groups The data varies with respect to type of the distribution, determined through the kurtosis, and variance covariance homogeneity (dispersion) The second data set used for this case is a real-life data set for prediction of bankruptcy ling for different rms
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We use two sources to identify a sample of rms that led for bankruptcy The rst source was the 1991 Bankruptcy Yearbook and Almanac, which lists bankruptcy listings in 1989 and 1990 The second source for bankruptcy lings was the Lexis/ Nexis database We used the following strategy to screen the rms for our bankruptcy data: 1 All lings for nancial companies and real estate investment trusts were excluded 2 Only lings by publicly traded rms were considered 3 Data on nancial ratios (identi ed later) are publicly available for 3 years prior to bankruptcy ling Our nal sample had bankruptcy lings from 1987 to 1992 The nancial data, for the rms identi ed in the sample, was obtained from the Compustat database Since we use classi cation models to predict bankruptcy, we used data that would be publicly available at the time of ling For each rm that led for bankruptcy, we identi ed a nonbankrupt rm with the same four-digit SIC code and total assets similar in size to the bankrupt rm All the nonbankrupt rms in the nal sample were publicly traded for 4 years prior and 3 years subsequent to the year of ling by the bankrupt rm To predict bankruptcy, we used ve nancial ratios These nancial ratios (predictor variables) were Earnings Before Interest and Taxes/Interest Expense, Earnings Before Interest and Taxes/Assets, Current Assets/Current Liabilities, Retained Earnings/Assets, and Market Value of Equity/Book Value of Debt The nal data set contained 100 total rms with 50 rms that had led for bankruptcy and 50 rms that did not le for bankruptcy We took 50 bankrupt and 50 nonbankrupt rms and randomly split them into two data sets The rst data set was the training data set that contained 25 bankrupt rms and 25 nonbankrupt rms (a total of 50 rms) The second data set was a holdout data set that contained the remaining 50 rms from the total original sample of 100 rms We compared the three different types of GAs with back-propagation based ANN (referred to as NN in the tables) and genetic programming (GP) approaches After initial experimentation, we used a population size of 100, a crossover rate of 03, and a mutation rate of 01 for our GA implementation Table 21 illustrates the training results of our experiments Tables 22 and 23 illustrate the results of three-way ANOVA for training performance of different techniques From Table 22, it can be seen that hypothesis 1 (variance heterogeneity) (F 2739:30) and hypothesis 2 (distribution kurtosis) (F 22:87) are supported (at the 001 level of signi cance) for learning (training) performance of the techniques Furthermore, the interactions between variance heterogeneity and kurtosis and between technique and variance heterogeneity were signi cant (at the level of signi cance 001), supporting as well hypothesis 3 From Table 23, it can be seen that hypothesis 4 is supported (at the 001 level of signi cance) with NN versus GA(O) (F 235:20), NN versus GA(U) (F 256:50), and NN versus GA(A) (F 206:42)
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