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Equipment Setup and Configuration It is important that the necessary hardware and software is (1) shipped, (2) delivered, (3) installed, and (4) configured correctly to reflect the requirements of the solution prior to the go -live date. The project manager needs to track and coordinate all IT equipment in support of the entire project implementation. I remember a project that was performing a web site migration and had a certain go-live date, but failed to have the hardware delivery dates confirmed with the suppliers. The hardware was delivered six weeks behind schedule and the team had to make use of replacements until the ordered hardware arrived. This example clearly shows the importance of the managing the IT equipment necessary for implementation. Even delivery schedules must be confirmed from third party contractors to commit to the master project schedule. Networks Access to Users Once the necessary solution is nearing the actual go-live date, the project manager should have a complete list of authorized users who will need access to the new system. This list is prepared prior to implementation because the task of obtaining user login passwords information can take time. The new IT solution should be configured to accept each authorized user of the new solution.

Progress in genetic knowledge and gene sequencing has renewed attention to the use of racial and ethnic classi cations in health care. Most scientists now believe that the categories of race and ethnicity are predominantly social and cultural constructs, rather than meaningful indicators of actual genetic difference. Little time has elapsed, on an evolutionary scale, since the ancestors common to all modern humans dispersed from Africa (Owens and King, 1999; Olson, 2001). It is markedly improbable that in that brief time geographically dispersed humans encountered selection pressures at once so strong and so materially different that group-speci c traits have evolved beyond those that in uence such super cial traits as skin coloration. Genetic research, however, has now shown that some allelic variations in disease susceptibility and in drug responsiveness correlate with race and ethnicity as commonly understood. Although race matters biologically hardly at all, in some instances it may be signi cant for the provision of good medical care to all population groups. Before exploring the ethical and legal standards for taking such differences into account, I rst describe the genomic developments that have raised this issue.

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Three matrices are required to summarize the ve difference equations. There are two column matrices representing the number of individuals at ages 1 5 at times t + 1 and t (vt+1 and vt respectively). These column matrices are referred to as the population-structure vectors or age-distribution vectors. There is also one square matrix, M, which gives all of the fecundity and survival values and is known as the population projection matrix. To check that equation 4.7 is equivalent to equations 4.2 4.6 you can multiply out the matrix and population-structure vector on the right-hand side of the equation. For readers unfamiliar with matrix multiplication, you begin by multiplying the ve coef cients in the top row of the square matrix M by the corresponding population sizes in the column matrix vt (0 N1, 0 N2, s0,1f3 N3 and so on) and add the resulting ve multiplied pairs of values to give N1 in vt+1. This process is then repeated with the next row, again multiplying by the corresponding values of N1 N5 in vt and summing the ve multiples. This process is repeated for all ve rows of the matrix M. Representation of age-structured populations in this manner was rst described by Bernardelli (1941), Lewis (1942) and Leslie (1945, 1948). Matrix equations such as equation 4.7, representing a set of difference equations, can be written in a general form to describe any age- or stagestructured population: v t +1 = Mv t (4.8)

Rapid prototyping, 96, 103 105 Rate of return, 228 Real-time trends, 65 Receivables, capital charge, 233 Reconciliation, 91 Recurring costs, 90 91 Regression analysis, 163, 255 Reject rates, 11 Relationship marketing, 156 Relevance Lost: The Decline of Managerial Accounting (Kaplan/Johnson), 35 Research and development (R&D), 101 Reserve balances, 220 Resource(s), generally: ABM cost assignment, 99 100 allocation, 48 consumption, 231 cost adjustments, 138 expenditures, 137 138 planning systems, 13 Resource consumption accounting (RCA), 135 Responsibility, 68 70 Retained earnings, 219 Return on investment (ROI), 5, 7, 27, 74, 151 153, 165, 167, 183, 186, 198, 230, 253 Reuther, Walter, 148 Revenue enhancement, 110, 116, 156 Reversibility, resource expenses, 138 Reward systems, 73 74 Risk management, 27, 258, 262 263 ROAIC (return on average investment in capital), 223 224 Rolling nancial forecasts, 76, 132, 134 Root-cause analysis, 24, 63 Rule-based costing, 171 Run-by-the-numbers management, 26 Sales force, automated, 159 160 Sales volume, implications of, 122 124 Sarbanes-Oxley Act, 217, 258 SAS Institute Inc., 64, 250, 260

channel management The process of coordinating supplies, production, and distribution with a specific sales channel. collaboration fashion. The process of jointly working together in a cooperative

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