Project report (M. Eng.)--Virginia Polytechnic Institute and State University, 1990. / Vita. Abstract. Includes bibliographical references (leaf 114). Also available via the Internet.

First, we consider a fundamental pricing model for a single type of reusable resource in which a fixed number of units are used to serve stochastically arriving customers. Customers choose to purchase the resource based on their willingness-to-pay and the current price. If purchased, occupy one unit of the reusable resources for a random amount of time. The firm seeks to maximize a weighted combination of profit, market share, and service level. We establish a series of theoretical results that characterize the strong universal performance of static pricing in such an environment. Second, we describe a comprehensive approach to pricing analytics for reusable resources in the context of rotable spare parts with an industrial partner. We discuss the process of instilling a new pricing culture and developing a scalable new pricing methodology at a major aircraft manufacturer. We develop a novel pricing analytics approach for all rotable spare parts. The new approach tackles the challenges of limited data availability, minimal demand information, and complex inventory dynamics. We also present a successful large-scale implementation of our approach which led to significant profit gains. Third, we extend the pricing model for reusable resources to the setting of multiple customer classes. We describe two types of heuristics for this class of problem with accompanying numerical experiments. In addition, we provide a universal performance guarantee for a special case. We also discuss the role of substitution effects between different classes of customers.

Submitted by HENRIQUE KRIGUER (henriquekriguer@uol.com.br) on 2016-02-15T11:56:47Z No. of bitstreams: 1 Dissertação de Mestrado Henrique Kriguer 2015 corrigido após qualificação.pdf: 1729817 bytes, checksum: 2ab0f19734b9d93a734ea53248959168 (MD5) / Approved for entry into archive by Ana Paula Grisoto (grisotoana@reitoria.unesp.br) on 2016-02-15T12:20:05Z (GMT) No. of bitstreams: 1 kriguer_h_me_guara.pdf: 1729817 bytes, checksum: 2ab0f19734b9d93a734ea53248959168 (MD5) / Made available in DSpace on 2016-02-15T12:20:05Z (GMT). No. of bitstreams: 1 kriguer_h_me_guara.pdf: 1729817 bytes, checksum: 2ab0f19734b9d93a734ea53248959168 (MD5) Previous issue date: 2015-12-21 / O gerenciamento de estoques torna-se cada vez mais importante em função da necessidade de aperfeiçoamento da eficiência na cadeia logística, que se traduz na redução de custos e aumento do nível de serviço. A literatura sobre o gerenciamento de estoques de peças de reposição é ampla, porém é pouco abordada em relação à escolha do modelo de classificação. Neste trabalho, o método Analytic Hierarchy Process (AHP) foi aplicado em uma Empresa de bens de capital para determinar o vetor de prioridade dos critérios adotados para a classificação de peças de reposição. Apresentamos, com base no resultado deste estudo o Índice de Reposição, que foi adotado como um fator de correção nos valores de estoque segundo a classificação ABC. O objetivo foi atingido com o aperfeiçoamento do método de classificação, tornando o trabalho do planejador de estoques melhor estruturado, permitindo que possa tomar a decisão de reabastecimento de forma estruturada conforme parâmetros pré estabelecidos. / The inventory management becomes increasingly important due to the need for improved efficiency in the supply chain, which translates into cost savings and increased service level. The literature on the management of spare parts inventory is wide, but is rarely addressed in relation to the choice of classification model. In this work, the Analytic Hierarchy Process (AHP) method was applied to a Company of capital goods to determine the priority vector of the criteria adopted for the spare parts classification. Here, based on the results of this study the Replacement Index, which was adopted as a correction factor in inventory values according to the ABC classification. The goal was achieved with the improvement of classification method, making the work of the best structured inventory planner, allowing can take the form of structured replenishment decision as pre-established parameters

Em diversos setores, em especial no automotivo, uma boa gestão dos estoques de peças de reposição tem impacto significativo na satisfação dos clientes e em sua fidelidade aos fabricantes. Neste trabalho foram estudadas diferentes políticas de gestão dos estoques de peças de reposição, para comparar seu desempenho e elaborar recomendações para seu uso. Foram comparados 17 conjuntos de políticas que envolvem diferentes abordagens no registro das demandas (dados individuais de cada pedido versus dados agregados em janelas de tempo semanais e mensais), modelos de previsão (média móvel, Croston modificado SBA) e diferentes formas de modelar a distribuição da demanda durante o Lead-time de ressuprimento (Normal, Gama, Binomial Negativa, composta Poisson-Normal, composta Poisson-Gama). Cada um desses 17 conjuntos de políticas foi simulado sob duas dinâmicas de reparametrização (mensal e semestral) e para quatro objetivos diferentes do nível de serviço (TFR: Target Fill Rate), totalizando 136 simulações para cada item do estoque (SKU). Foram considerados 10.032 SKU\'s de uma montadora de automóveis instalada no Brasil, com um histórico de seis anos de movimentação. Diferentes recomendações foram elaboradas conforme categorização dos itens já existente na literatura. Os resultados apontaram recomendações distintas para cada TFR, incluindo combinações de todas as alternativas estudadas, descartando apenas o uso das distribuições Normal, composta Poisson-Normal e composta Poisson-Gama. Sugere-se que as recomendações sirvam como guia para o uso desses modelos pelos praticantes. / In many areas, including automotive, a good spare parts inventory management can substantially affect customer satisfaction and their loyalty to the brands. Different spare parts inventory control policies were evaluated in this study, aiming to compare their performance and write recommendations for their usage. Seventeen policy sets were compared, including different approaches in recording demand data (individual orders data against time bucket records weekly and monthly), different demand forecasting methods (simple moving average, Syntetos-Boylan-approximation SBA) and different models for demand distribution during lead-time (Normal, Gama, Negative Binomial, compound Poisson-Normal, compound Poisson-Gama). Each policy set was simulated under two revision frequencies (monthly and semi-annually) and four different Target-Fill-Rates (TFR), totalizing 136 simulation runs for each SKU. Database included movement of 10.032 SKU´s during last 6 years from an automaker installed in Brazil. Results pointed different recommendations for existing classification schemes and under each TFR. Recommendations included all studied alternatives, discarding only the usage of Normal, compound Poisson-Normal and compound Poisson-Gama for demand distribution during lead-time. Practitioners are stimulated to use these recommendations as a guideline.

Em diversos setores, em especial no automotivo, uma boa gestão dos estoques de peças de reposição tem impacto significativo na satisfação dos clientes e em sua fidelidade aos fabricantes. Neste trabalho foram estudadas diferentes políticas de gestão dos estoques de peças de reposição, para comparar seu desempenho e elaborar recomendações para seu uso. Foram comparados 17 conjuntos de políticas que envolvem diferentes abordagens no registro das demandas (dados individuais de cada pedido versus dados agregados em janelas de tempo semanais e mensais), modelos de previsão (média móvel, Croston modificado SBA) e diferentes formas de modelar a distribuição da demanda durante o Lead-time de ressuprimento (Normal, Gama, Binomial Negativa, composta Poisson-Normal, composta Poisson-Gama). Cada um desses 17 conjuntos de políticas foi simulado sob duas dinâmicas de reparametrização (mensal e semestral) e para quatro objetivos diferentes do nível de serviço (TFR: Target Fill Rate), totalizando 136 simulações para cada item do estoque (SKU). Foram considerados 10.032 SKU\'s de uma montadora de automóveis instalada no Brasil, com um histórico de seis anos de movimentação. Diferentes recomendações foram elaboradas conforme categorização dos itens já existente na literatura. Os resultados apontaram recomendações distintas para cada TFR, incluindo combinações de todas as alternativas estudadas, descartando apenas o uso das distribuições Normal, composta Poisson-Normal e composta Poisson-Gama. Sugere-se que as recomendações sirvam como guia para o uso desses modelos pelos praticantes. / In many areas, including automotive, a good spare parts inventory management can substantially affect customer satisfaction and their loyalty to the brands. Different spare parts inventory control policies were evaluated in this study, aiming to compare their performance and write recommendations for their usage. Seventeen policy sets were compared, including different approaches in recording demand data (individual orders data against time bucket records weekly and monthly), different demand forecasting methods (simple moving average, Syntetos-Boylan-approximation SBA) and different models for demand distribution during lead-time (Normal, Gama, Negative Binomial, compound Poisson-Normal, compound Poisson-Gama). Each policy set was simulated under two revision frequencies (monthly and semi-annually) and four different Target-Fill-Rates (TFR), totalizing 136 simulation runs for each SKU. Database included movement of 10.032 SKU´s during last 6 years from an automaker installed in Brazil. Results pointed different recommendations for existing classification schemes and under each TFR. Recommendations included all studied alternatives, discarding only the usage of Normal, compound Poisson-Normal and compound Poisson-Gama for demand distribution during lead-time. Practitioners are stimulated to use these recommendations as a guideline.

Reuse of components as opposed to material recovery, recycling or disposal has been identified as one of the most efficient EOL strategies for products. The concept behind reuse is that some components and subassemblies have a design life that exceeds the life of the product itself. In order for reuse to be successfully implemented as an EOL strategy, a designer needs to incorporate into a product a philosophy of Design for Reuse (DfRe) at the early design stage. Reliable methods to assess the remaining life of used components based on a products usage life are also required. Furthermore, current industry practices and literature advocate that there is no methodology to decide which parameters need to be redesigned so as to change the life of a selected component to a desired level. The objective of this research is to develop a methodology to assess the reuse potential of product groups based on component failure mechanisms and their associated critical lifetime prediction design parameters. Utilising these clustered groups mathematical models were then developed to establish the useful life of the components for each clustered group. Finally, a means of equating useful life to design life was established and the relationship between, the failure mechanisms, critical lifetime prediction design parameters and design life were represented in graphical format. In order to achieve the proposed objective, Cluster analysis, in particular Group Technology (GT) and Hierarchical clustering were employed to group components with similar failure mechanisms. Following this, multiple linear regression was used to establish mathematical models based on condition monitoring data for each of the clustered groups and their related critical lifetime prediction design parameters. A sensitivity analysis was conducted using the mathematical models, in order to produce graphical relations between the useful life and design parameters of a product. The validity of the suggested methodology was tested on electric motors and a gearbox as both these components have demonstrated great reuse potential. The results demonstrate that the methodology can assist designers in estimating the design life and associated design parameters with great accuracy, and subsequently aiding in a stratagem for reuse.

<p>This paper analyses the pricing on spare parts. Empirical studies have showed that manufacturers of durable goods make an unproportional large profit on its spare parts in relation to the revenue it generates. It is first showed that according to the standard economic model the price on spare part ought to be zero since the producer include an insurance in the price of the main good. Further it is showed that moral hazard alone do not explain the pricing found in the studies. Finally an analysis of whether consumers with present-biased preferences could be a possible explanation is made. The analysis finds that it is a possibility however somewhat unlikely.</p>

This paper analyses the pricing on spare parts. Empirical studies have showed that manufacturers of durable goods make an unproportional large profit on its spare parts in relation to the revenue it generates. It is first showed that according to the standard economic model the price on spare part ought to be zero since the producer include an insurance in the price of the main good. Further it is showed that moral hazard alone do not explain the pricing found in the studies. Finally an analysis of whether consumers with present-biased preferences could be a possible explanation is made. The analysis finds that it is a possibility however somewhat unlikely.

Reuse of components as opposed to material recovery, recycling or disposal has been identified as one of the most efficient EOL strategies for products. The concept behind reuse is that some components and subassemblies have a design life that exceeds the life of the product itself. In order for reuse to be successfully implemented as an EOL strategy, a designer needs to incorporate into a product a philosophy of Design for Reuse (DfRe) at the early design stage. Reliable methods to assess the remaining life of used components based on a products usage life are also required. Furthermore, current industry practices and literature advocate that there is no methodology to decide which parameters need to be redesigned so as to change the life of a selected component to a desired level. The objective of this research is to develop a methodology to assess the reuse potential of product groups based on component failure mechanisms and their associated critical lifetime prediction design parameters. Utilising these clustered groups mathematical models were then developed to establish the useful life of the components for each clustered group. Finally, a means of equating useful life to design life was established and the relationship between, the failure mechanisms, critical lifetime prediction design parameters and design life were represented in graphical format. In order to achieve the proposed objective, Cluster analysis, in particular Group Technology (GT) and Hierarchical clustering were employed to group components with similar failure mechanisms. Following this, multiple linear regression was used to establish mathematical models based on condition monitoring data for each of the clustered groups and their related critical lifetime prediction design parameters. A sensitivity analysis was conducted using the mathematical models, in order to produce graphical relations between the useful life and design parameters of a product. The validity of the suggested methodology was tested on electric motors and a gearbox as both these components have demonstrated great reuse potential. The results demonstrate that the methodology can assist designers in estimating the design life and associated design parameters with great accuracy, and subsequently aiding in a stratagem for reuse.

Reuse of components as opposed to material recovery, recycling or disposal has been identified as one of the most efficient EOL strategies for products. The concept behind reuse is that some components and subassemblies have a design life that exceeds the life of the product itself. In order for reuse to be successfully implemented as an EOL strategy, a designer needs to incorporate into a product a philosophy of Design for Reuse (DfRe) at the early design stage. Reliable methods to assess the remaining life of used components based on a products usage life are also required. Furthermore, current industry practices and literature advocate that there is no methodology to decide which parameters need to be redesigned so as to change the life of a selected component to a desired level. The objective of this research is to develop a methodology to assess the reuse potential of product groups based on component failure mechanisms and their associated critical lifetime prediction design parameters. Utilising these clustered groups mathematical models were then developed to establish the useful life of the components for each clustered group. Finally, a means of equating useful life to design life was established and the relationship between, the failure mechanisms, critical lifetime prediction design parameters and design life were represented in graphical format. In order to achieve the proposed objective, Cluster analysis, in particular Group Technology (GT) and Hierarchical clustering were employed to group components with similar failure mechanisms. Following this, multiple linear regression was used to establish mathematical models based on condition monitoring data for each of the clustered groups and their related critical lifetime prediction design parameters. A sensitivity analysis was conducted using the mathematical models, in order to produce graphical relations between the useful life and design parameters of a product. The validity of the suggested methodology was tested on electric motors and a gearbox as both these components have demonstrated great reuse potential. The results demonstrate that the methodology can assist designers in estimating the design life and associated design parameters with great accuracy, and subsequently aiding in a stratagem for reuse.