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customer commitment and preference. Companies treat post-sales service as a cost against the initial sale and a threat to their precious margins, as opposed to regarding it as the most affordable sales enabler for the next purchase. This is not surprising, considering that most companies do not have a detailed plan for what to sell the customer beyond the first sale. They treat the customer as a destination that, once reached, is in safe territory and taken for granted. Customers hate to be taken for granted. When they sense it, they defect. When we present this paradox model in our seminars, we often equate it to personal life. In the beginning both sides are investing resources, money, and creativity to lure the other side; when a commitment is achieved, there is an immediate decrease in investment. One party feels as if the pressure is off, and it s time to return to some boring existence. Then, just as the other party, who feels neglected and disappointed, is about to leave the relationship, the neglecting party tries desperate measures like buying a grand gift. This quick fix doesn t usually work it doesn t address the underlying problem of lack of willingness to invest for the long term. This analogy usually manages to get the audience to understand and relate to the paradox. Over time, companies become comfortable with this model. They employ actuaries to predict how many customers they can churn so they
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Finch et al. (1989) describe a test of global optimality, similar to grid search techniques, which is based on an evaluation of the local maximum likelihood function from randomly selected starting points. Veall (1990) describes another way of testing the hypothesis, using a similar set of local optima. Unlike a grid search, these tests provide a way to formally test the hypothesis that the optimum found is global. Derigs (1985) has developed a technique, which he uses for discrete optimization, of testing for global optima given an external, analytically derived upper bound for a function. Den Haan and Marcet (1994) describe a technique for testing the accuracy of complex econometric simulation results, through examination of the distribution of the residuals. White (1981,1982) has developed a test for misspeci cation of the maximum likelihood models based on the divergence between covariance matrices computed from the Hessian and from the cross-product of rst derivatives (respectively). We discuss these tests in more detail in 4. These tests are not in wide use and to our knowledge have not been incorporated into any statistical software package.
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Marketing, Not Manufacturing Consumer brands are becoming increasingly important. In fact, brands today are perhaps more important than the actual products themselves. Take for instance consumer apparel companies such as L.L. Bean, Lands End, and Nike. Largely, their days of owning massive manufacturing plants to make clothing, shoes, and other goods are behind them. Instead these companies, which have powerful brand recognition and spend millions annually to ensure it, design products and create manufacturing specifications, while contracting out the manufacturing. By turning to partner companies for help, while maintaining the perceived value of their own brands, these companies capitalize on the strengths of their partners while retaining their focus on what they do best. This is how companies will work together in the future as they join adaptive business networks.
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The class diagram for the Component Configurator pattern is as follows:
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Optimizing an organization s value chain begins with understanding customer demand. Consider this observation: In the value chain there is only one independent decision being made, and all of the other thousands of decisions are dependent ones. What is that single independent decision It is the ultimate end-consumer choosing to exchange his money with a purchase. Subsequent to that, all stick replenishment and service decisions, loaded with production and logistics decisions, are governed. This is so profound that it makes me wonder why the term supply chain management was not initially coined as demand chain management. The performance management (PM) tools are enablers for the value chain intelligence suite of solutions. This solutions suite includes demand planning, inventory replenishment planning, price optimization, and spend management: Demand planning. Reliable forecasting is the key. Statistically sound large-scale demand forecasts serve as a foundation for all levels of business planning. Effective forecasting tools dynamically adjust to irregular demand series and also take into account the impact of sales promotions. This must be done for every item at every level of detail because one shortage can prevent an assembled product. Hence the need for a large-scale forecasting system. A strong competency in demand planning provides the foundation for demand creation generating higher pro ts with customerin uencing techniques. Inventory replenishment. Using reliable forecasts, economic-aware replenishment strategies are the key. That is, desired levels of service are blended with alternative rules or policies for different types of items, such as between repeatedly ordered, fast-moving items or their opposite types. Replenishments ideally should be planned by categories, often down to the item-by-item plans; and multiscenario analysis should allow exploration and comparison of different options. Here detail is needed because sometimes you have to get close down to the bottom where the dirt crops
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and D (45 ) planes corresponding to the TEmn or TMmn grazing lobes can be computed from the following equations: n 0 2 m 0 1 (8.1) 1 E = sin a b n 0 m 0 2 1 (8.2) 1 H = sin b a
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Let s look rst at the rami cations of substituting in the error model for Y: (Y ) = X + Y = X + ( + ). (4.5)
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Assessment Procedures
9: Search Engine Marketing (SEM) 227
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