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.

Wong, Chun Hung Eliphas. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 74-78). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Thesis/Assessement Committee --- p.vi / List of Figures --- p.xi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Literature Review --- p.13 / Chapter 1.2 --- K-Convexity --- p.16 / Chapter 1.2.1 --- "The (s, S)policy and cardinal optimality equation form" --- p.17 / Chapter 2 --- Inventory Problem --- p.25 / Chapter 2.1 --- Two-channel Inventory Model --- p.26 / Chapter 2.1.1 --- Model Formulation --- p.27 / Chapter 2.2 --- The total expected cost and constraints --- p.34 / Chapter 2.3 --- The optimality equation --- p.37 / Chapter 3 --- The two phase optimal policy --- p.40 / Chapter 3.1 --- Deviation of two phase optimal policy --- p.41 / Chapter 3.1.1 --- "First phase optimization - The (s, S, s') policy" --- p.42 / Chapter 3.1.2 --- Second phase optimization --- p.46 / Chapter 3.2 --- More about the optimal policy --- p.60 / Chapter 4 --- Further discussion and conclusion --- p.64 / Chapter 4.1 --- Multi-period problem --- p.65 / Chapter 4.1.1 --- Model formulation --- p.65 / Chapter 4.1.2 --- The challenges in extending the optimal policy --- p.67 / Chapter 4.2 --- Conclusion --- p.69 / Bibliography --- p.74

Abstract not available

Decisions on capacity are often treated separately from those of production and inventory. In most situations, capacity issues are longer-term, so capacity-related decisions are considered strategic and thus not part of supply planning. This research focuses on optimal supply planning with emphasis on variable capacity to meet uncertain demand. It also defines three levels of capacity change: operating hours, labor availability and production hardware availability. The work presented here deals with the fundamental decisions to determine capacity, production, and inventory to meet customer demand while optimizing revenue and costs over a planning horizon (typically the life of the product). With the Lagrangian technique for constrained optimization, it can be shown that the optimal supply capacity has upper and lower bounds. The optimal feedback policy prescribes increasing the supply capacity when at the beginning of the planning interval it is below the lower bound. Similarly, the supply capacity should be decreased to the upper bound when it is above the upper bound. This paper will present arguments for characterizing forecast evolution and information sharing in the supply chain to obtain a predictor-corrector approach to supply chain control. / Singapore-MIT Alliance (SMA)

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 159-164).

We analyze the problem of distributing units of a product, by a capacitated vehicle, from one storage location (depot) to multiple retailers. The demand processes at the retailers are stochastic and time-dependent. Based on current inventory information, the decision maker decides how many units of the product to deposit at the current retailer, or pick up at the depot, and which location to visit next. We refer to this problem as the stochastic vendor managed inventory (SVMI) problem. In the Markov decision process model of the SVMI problem, we show how a retailer continues to be the vehicle's optimal destination as inventory levels of the retailers vary. Furthermore, an optimal inventory action is shown to have monotone relations with the inventory levels. The multi-period SVMI problem and the infinite horizon (periodic) SVMI problem are analyzed. Additionally, we develop three suboptimal solution procedures, complete a numerical study, and present a case study, which involves a distribution problem at the Coca-Cola Enterprises, Inc. We consider four variations of the SVMI problem, which differ in the available state information and/or the vehicle routing procedure. Analytically, we compare the optimal expected total rewards for the SVMI problem and its variations. Our computational experience suggests a complementary relationship between the quality of state information and the size of the set of retailers that the vehicle can visit.

Thesis (M.S.)--Mississippi State University. Department of Industrial Engineering. / Title from title screen. Includes bibliographical references.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Systems Science and Industrial Enngineering, 2009. / Includes bibliographical references.

No description available.

No description available.