Internet-based customization tools can be used to design service encounters that maximize customers' utility in the present or explore their tastes to provide more value in the future, where these two goals conflict with each other. Maximizing expected customer satisfaction in the present leads to slow rates of learning that may limit the ability to provide quality in the future. An emphasis on learning can lead to unsatisfied customers that will not only forego purchasing in the current period, but, more seriously, never return if they lose trust in the service provider's ability to meet their needs. This paper describes service design policies that balance the objectives of learning and selling by characterizing customer lifetime value as a function of knowledge. The analysis of the customization problem as a dynamic program yields three results. The first result is the characterization of customization policies that quantify the value of knowledge so as to adequately balance the expected revenue of present and future interactions. The second result is an analysis of the impact of operational decisions on loyalty, learning, and profitability over time. Finally, the quantification of the value of knowing the customer provides a connection between customer acquisition and retention policies, thus enhancing the current understanding of the mechanisms connecting service customization, value creation, and customer lifetime value.

Incerteza nos sistemas de manufatura tem sido há muito tempo fonte de complexidade gerencial. Neste artigo discutimos o impacto de diferentes fontes de incerteza e apresentamos um metodologia para avaliar tal impacto no comportamento do sistema. Introduzimos o conceito de curvas de tradeoff como uma característica de um sistema de manufatura e ilustramos sua utilização para tomar decisões com respeito à quantidade e tipo de capacidade necessária para gerir o sistema eficientemente, para avaliar o impacto de incerteza na chegada e processamento de produtos, assim como as conseqüências de mudanças na taxa média de produção e no mix de produtos. A metodologia é ilustrada com um exemplo derivado de uma aplicação real numa indústria de semicondutores.

Uncertainty in manufacturing systems has long been the source of managerial complexity. In this paper we discuss the impact of different sources of uncertainty and present a methodology to assess their impact on system behavior. We introduce the concept of tradeoff curves as a characteristic of a manufacturing system and illustrate their use to make decisions concerning the amount and type of capacity necessary to manage the system efficiently, to assess the impact of products arrival and processing uncertainties, as well as the consequences of changes in throughput and product mix. The methodology is illustrated with an example derived from an actual application in the semiconductor industry.

Este artigo apresenta a segunda (e última) parte do nosso exame dos modelos de redes de filas abertas aplicados a sistemas de manufatura discretos. Nosso enfoque é em modelos de projeto e planejamento de job-shops. Na primeira parte (BITRAN & MORABITO, 1995b) revisamos métodos de decomposição exatos e aproximados para modelos de avaliação de desempenho em sistemas com múltiplas classes de produtos e diversas estações de trabalho. Nesta segunda parte examinamos modelos de otimização de três categorias de problemas: a primeira minimiza o investimento de capital de maneira a atingir uma medida de desempenho (estoque em processo ou leadtime), a segunda busca otimizar a medida de desempenho sujeito às restrições de recursos, e a terceira explora resultados de pesquisas recentes com a redução de complexidade mediante reprojeto da planta e da partição de produtos.

This paper presents the second (and last) part of our survey on open queueing network models applied to discrete manufacturing systems. We focus on design and planning for job-shops. In the first part (Bitran and Morabito, 1995b) we reviewed exact and approximate decomposition methods for performance evaluation models for single and multiple product class systems. The second part reviews optimization models of three categories of problems: the first minimizes capital investment subject to attaining a performance measure (WIP or leadtime), the second seeks to optimize the performance measure subject to resource constraints, and the third explores recent research developments in complexity reduction through shop redesign and products partitioning.