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Resources are allocated People are the central focus Finally, every training course is aligned with the business it serves and education is customized for each person to maximize potential Each of the propositions is supported by the case However, analysis of the data revealed more depth than what is currently in the literature This study can be extended by modifying or rethinking the propositions based on what was uncovered by the case The rst proposition states that a systematic methodology will facilitate change efforts Fannie Mae uses the University Model to develop people, align classes to business objectives on the job, and enable equitable evaluation based on training The model is also used to keep technical training exible A natural extension of this study is to explore the impact of these human and technical factors on change management People development concentrates on two dimensions human and technical Human development encompasses behaviors, personality traits, and attitudes Technical development encompasses skills, knowledge, and competencies For real change to occur, Fannie Mae has surmised that people must understand why change is important to the business Hence, IT training is not limited to basic C or SYBASE A curriculum is designed with a Fannie Mae slant; that is, it re ects what people will do back on the job It also includes con ict resolution, creativity, communication, and teamwork training to develop the soft side of its technical people Personality tests are given to people to help customize training to the individual Fannie Mae also believes that behavior can be shaped if it hires people with the right attitude Technical development focuses on skills, knowledge, and competencies Skill and knowledge development is pretty straightforward because the model helps match training with business The second proposition states that top management support is critical to successful change The case supports this proposition The CIO is the champion and visionary of training transformation at Fannie Mae He hired Gus to help him implement his vision The president is supportive of the University Model and budgets generously for IT training (approximately $7 million per year) Two other important factors are risk-taking and mistakes Top management is happy if training hits the mark 20% of the time because of dramatic increases in people productivity This means that 80% of the time mistakes are made Allowing mistakes as a natural part of the learning process encourages risk-taking and innovation Researchers can augment this study by gathering more data about each factor The third proposition states that a strategy-driven approach will facilitate transformation Transformation at Fannie Mae focuses on its training paradigm because it believes that people are the key to success The idea is to train information system (IS) professionals so that they can deliver whatever work, services, and products the business needs Training works with business managers to develop a curriculum that is strategically aligned with the business needs of the enterprise Exploration of training business partnerships and strategic alignment of the curriculum can further transformation research.

Form W-4

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where f is the cost (eg length) of the theoretical optimum solution This provides a theoretical value for the upper bound, max Since the optimal solution is generally not known, and therefore f is an unknown value, MMAS initializes to an x x estimate of f , by setting f = f ( (t)), where f ( (t)) is the cost of the global-best path This means that the maximum value for pheromone concentrations changes as soon as a new global-best position is found The pheromone upper bound, max (t) = is therefore time-dependent St tzle and Hoos [816] derive formally that sensible values for the lower bound on u pheromone concentrations can be calculated using max (t)(1 pnG ) min (t) = (1727) (nG /2 1) pnG where p is the probability at which the best solution is constructed Note that p < 1 to ensure that min (t) > 0 Also note that if p is too small, then min (t) > max (t) In such cases, MMAS sets min (t) = max (t), which has the e ect that only the heuristic information, ij , is used The value of p is a user-de ned parameter that needs to be optimized for each new application During the initialization step of the MMAS algorithm (refer to Algorithm 175), all pheromone concentrations are initialized to the maximum allowed value, max (0) = max 1 1 1 f ( (t)) x (1726)

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