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Integrated Business Processes and Technology Companies seeking to create an adaptive business network typically ask themselves many questions. How can we coordinate our business processes to work together faster How can we eliminate the bottlenecks that prevent us from getting our products and services to the customer more quickly What customer information should we share to our mutual benefit What links companies together in an adaptive business network are standardized business processes. By forming close procedural links, participants within a network put the mechanisms in place to f lexibly respond to changing market conditions. Rather than merely placing or filling orders from their next-in-line supplier or customer, participants work collaboratively to plan for, anticipate, and respond to changes in customer demand. This change from the traditional buyer-seller relationship requires a fundamental shift in how a company s managers operate their business units and measure their success. It also requires a shift in the cultural thinking of the organization. For example, managers of a company must change how they negotiate with their partners. They also must change how the company purchases products, how it plans its production schedules, and how it allocates its capital resources. Managers of a company must change their perspective to focus on the customer who purchases the finished goods produced by the network. They must change how they measure customer satisfaction and establish new measurements of success that are aligned to the needs of that customer.

Nicholson, 70 Nickels, 521 Nicolson, 16, 20, 23, 163, 164 Nigg, 123, 124, 127 Nilsson, 433, 434, 435, 438 Nittrouer, 67 Noeker, 247 Noland, 368 Noll, 216, 217, 437 Nomura, 150 Noordam, 424 Northam, 479, 480 Nosarti, 309, 314 Nussbaum, 9 Nuzzolo-Gomez, 107

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3. I am indebted to Kirsten Foot for directing my attention to Henke s work, and for pointing out the close similarity between my concept of mending and that of repair. 4. Mindful of Heraclitus dictum that you cannot step into the same river twice, I do not mean that the status quo is the result, rather that is the intention. 5. I am indebted to Annalisa Tota for suggesting a link between our two lines of inquiry during our long summer conversations. Responsibility for transposing memory work to analysis of organizations is entirely mine, although I regard it as a good example of cross-fertilization between apparently distant areas of analysis.

If we consider a novice learning a trade, we may imagine that during his learning trajectory he also learns to develop a performed self (Goffman, 1959), so that individual and social learning are somehow interconnected. Learning a trade means also developing one or more social identities. Occupational and organizational social identities are built upon individual identity but they have highly signi cant social referents, for the de nitions which individuals give to their selves are also based on group or other social categories to which they feel that they belong and on the situations in which they nd themselves. The relationship of social identity to organizational learning is still largely unexplored, although we know that the social identity of organizational groups is vested in the systems and bodies of knowledge that they perceive they own.Their members attribute symbolic value to that knowledge and regard themselves as having a right to arbitrate over this value (Child and Rodrigues, 2003: 540); but since the ways social identities and organizational learning interact are complex they have to be exam-

Nonparametric densities use no parameters at all. We illustrate the nonparametric density by the example of the empirical CDF (ecdf) as follows. It is easier to visualize the ecdf which changes from 0 to 1, rather than the density f(x). The observed data xt with T observations can be rst arranged in increasing order of magnitude to de ne the so-called order statistics, x(t). Note that min(xt) = x(1) and max(xt) = x(T). Next, one assumes that the empirical data dictate the underlying density for the random variable X without any exibility beyond the observed values themselves. Then the probability Pr(X < x(1)) = 0 = Pr(X = x(T)). The construction of the empirical CDF does not permit any intermediate values either. Thus the ecdf = 0 if (X < x(1)). It jumps to the height of (1/T) at (X = x(1)) and remains at until (X = x(2)), at which time it jumps to the height 2/T and remains at until (X = x(3)), and so on. At (X = x(T)) it attains its full height = T/T or 1, and remains there for all (X > x(T)). The value at risk for a nonparametric density is obtained by inverting the ecdf as VaR(a ) = - K[ecdf ] (a ),

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usually have the option to choose the seed, but they involve a few extra programming steps DIEHARD Tests Under a grant from the National Science Foundation, G Marsaglia of the Florida State University developed a battery of tests for checking whether the alleged uniform random numbers are truly uniform Marsaglia (1996) posts 18 tests called DIEHARD on the Internet to reveal hidden defects among the pseudouniform random numbers produced by a software package Details regarding such tests are too technical for our purposes and best omitted for brevity It is possible to test whether any given software satis es the DIEHARD tests.

here we re trying to describe a complex business that is often very unpredictable, not computer software that, when written correctly, is very predictable. If a model contains many processes, the processes can be allocated to packages to organize the model, as shown in Figure 4.11. Recall that in UML, a package is a generic grouping of elements. It is up to the modeler to determine which packages are necessary and what they represent. The packages could be mapped to represent the organization of the company, the different types of processes, or a number of abstractions above the process level.

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